Wet Gas Measurement

ABSTRACT

A multi-phase process fluid is passed through a vibratable flowtube. Motion is induced in the vibratable flowtube. A first apparent property of the multi-phase process fluid based on the motion of the vibratable flowtube is determined, and an apparent intermediate value associated with the multi-phase process fluid based on the first apparent property is determined. A corrected intermediate value is determined based on a mapping between the intermediate value and the corrected intermediate value. A phase-specific property of a phase of the multi-phase process fluid is determined based on the corrected intermediate value.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation (and claims the benefit of priority under 35 U.S.C. §120) of U.S. patent application Ser. No. 12/614,156, filed Nov. 6, 2009 and titled WET GAS MEASUREMENT, now allowed, as U.S. Pat. No. 8,109,152, and this application is a continuation (claims the benefit of priority under 35 U.S.C. §120) of U.S. patent application Ser. No. 11/846,393, filed Aug. 28, 2007 and titled WET GAS MEASUREMENT, now allowed, as U.S. Pat. No. 7,617,055, which claims the benefit of U.S. Provisional Application Ser. No. 60/823,753, filed Aug. 28, 2006, and titled WET GAS MEASUREMENT SYSTEM and the benefit of U.S. Provisional. Application Ser. No. 60/913,148 filed Apr. 20, 2007, and titled WET GAS CALCULATIONS. All of these applications are incorporated by reference in their entirety.

TECHNICAL FIELD

This description relates to flowmeters.

BACKGROUND

Flowmeters provide information about materials being transferred through a conduit. For example, mass flowmeters provide a direct indication of the mass of material being transferred through a conduit. Similarly, density flowmeters, or densitometers, provide an indication of the density of material flowing through a conduit. Mass flowmeters also may provide an indication of the density of the material.

Coriolis-type mass flowmeters are based on the well-known Coriolis effect, in which material flowing through a rotating conduit becomes a radially traveling mass that is affected by a Coriolis force and therefore experiences an acceleration. Many Coriolis-type mass flowmeters induce a Coriolis force by sinusoidally oscillating a conduit about a pivot axis orthogonal to the length of the conduit. In such mass flowmeters, the Coriolis reaction force experienced by the traveling fluid mass is transferred to the conduit itself and is manifested as a deflection or offset of the conduit in the direction of the Coriolis force vector in the plane of rotation.

Energy is supplied to the conduit by a driving mechanism that applies a periodic force to oscillate the conduit. One type of driving mechanism is an electromechanical driver that imparts a three proportional to an applied voltage. In an oscillating flowmeter, the applied voltage is periodic, and is generally sinusoidal. The period of the input voltage is chosen so that the motion of the conduit matches a resonant mode of vibration of the conduit. This reduces the energy needed to sustain oscillation. An oscillating flowmeter may use a feedback loop in which a sensor signal that carries instantaneous frequency and phase information related to oscillation of the conduit is amplified and fed back to the conduit using the electromechanical driver.

SUMMARY

In one general aspect, a multi-phase process fluid is passed through a vibratable flowtube. Motion is induced in the vibratable flowtube. A first apparent property of the multi-phase process fluid based on the motion of the vibratable flowtube is determined, and an apparent intermediate value associated with the multi-phase process fluid is determined based on the first apparent property. A corrected intermediate value is determined based on a mapping between the apparent intermediate value and the corrected intermediate value. A phase-specific property of a phase of the multi-phase process fluid is determined based on the corrected intermediate value.

Implementations may include one or more of the following features. The mapping may be a neural network configured to determine an error in the intermediate value resulting from the presence of the multi-flow process fluid. The apparent intermediate value may be determined to be within a first defined region of values prior to determining the corrected intermediate value, and the corrected intermediate value may be determined to be within a second defined region of values prior to determining the phase-specific property of a phase of the multi-phase process fluid.

The multi-phase process fluid may be a wet gas. The multi-phase process fluid may include a first phase and a second phase, the first phase may include a non-gas fluid, and the second phase may include a gas. The multi-phase process fluid may include a first phase including a first non-gas fluid, and a second phase including a second non-gas fluid, and a third phase including a gas.

Determining the first apparent property of the multi-phase process fluid may include determining a second apparent property of the multi-phase process fluid. The first apparent property of the multiphase process fluid may be a mass flow rate and the second apparent property may be a density.

One or more measurements corresponding to an additional property of the process fluid may be received. The additional property of the multi-phase fluid may include one or more of a temperature of the multi-phase fluid, a pressure associated with the multi-phase fluid, and a watercut of the multi-phase fluid, and determining an apparent intermediate value associated with the multi-phase process fluid based on the first apparent property may include determining the intermediate value based on the first apparent property and the additional property.

Determining an apparent intermediate value associated with the multi-phase process fluid based on the first apparent property may include determining a volume fraction associated with an amount of non-gas fluid in the multi-phase process fluid and a volumetric flow rate of the multi-phase fluid. Determining an apparent intermediate value associated with the multi-phase process fluid based on the first apparent property may include determining a first Froude number corresponding to a non-gas phase of the multi-phase fluid and a second Froude number corresponding to a gas phase of the multi-phase fluid.

Determining a phase-specific property of a phase of the multi-phase process fluid based on the corrected intermediate value may include determining a mass flow rate of a non-gas phase of the multi-phase fluid.

Implementations of any of the techniques described above may include a method or process, a system, a flowmeter, or instructions stored on a storage device of flowmeter transmitter. The details of particular implementations are set forth in the accompanying drawings and description below. Other features will be apparent from the following description, including the drawings, and the claims.

Implementations of any of the techniques described above may include a method or process, a system, a flowmeter, or instructions stored on a storage device of flowmeter transmitter. The details of particular implementations are set forth in the accompanying drawings and description below. Other features will be apparent from the following description, including the drawings, and the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a digital mass flowmeter.

FIGS. 2A and 2B are perspective and side views of mechanical components of a mass flowmeter.

FIGS. 3A-3C are schematic representations of three modes of motion of the flowmeter of FIG. 1.

FIG. 4 is a block diagram of an analog control and measurement circuit,

FIG. 5 is a block diagram of a digital mass flowmeter.

FIG. 6 is a flow chart showing operation of the meter of FIG. 5.

FIGS. 7A and 7B are graphs of sensor data.

FIGS. 8A and 8B are graphs of sensor voltage relative to time.

FIG. 9 is a flow chart of a curve fitting procedure.

FIG. 10 is a flow chart of a procedure for generating phase differences.

FIGS. 11A-11D, 12A-12D, and 13A-13D illustrate drive and sensor voltages at system startup.

FIG. 14 is a flow chart of a procedure for measuring frequency, amplitude, and phase of sensor data using a synchronous modulation technique.

FIGS. 15A and 15B are block diagrams of a mass flowmeter.

FIG. 16 is a flow chart of a procedure implemented by the meter of FIGS. 15A and 15B.

FIG. 17 illustrates log-amplitude control of a transfer function.

FIG. 18 is a root locus diagram.

FIGS. 19A-19D are graphs of analog-to-digital converter performance relative to temperature.

FIGS. 20A-20C are graphs of phase measurements.

FIGS. 21A and 21B are graphs of phase measurements.

FIG. 22 is a flow chart of a zero offset compensation procedure.

FIGS. 23A-23C, 24A, and 24B are graphs of phase measurements.

FIG. 25 is a graph of sensor voltage.

FIG. 26 is a flow chart of a procedure for compensating for dynamic effects.

FIGS. 27A-35E are graphs illustrating application of the procedure of FIG. 29.

FIGS. 36A-36L are graphs illustrating phase measurement.

FIG. 37A is a graph of sensor voltages.

FIGS. 37B and 37C are graphs of phase and frequency measurements corresponding to the sensor voltages of FIG. 37A.

FIGS. 37D and 37E are graphs of correction parameters for the phase and frequency measurements of FIGS. 37B and 37C.

FIGS. 38A-38H are graphs of raw measurements.

FIGS. 39A-39H are graphs of corrected measurements.

FIGS. 40A-40H are graphs illustrating correction for aeration.

FIG. 41 is a block diagram illustrating the effect of aeration in a conduit.

FIG. 42 is a flow chart of a setpoint control procedure.

FIGS. 43A-43C are graphs illustrating application of the procedure of FIG. 41.

FIG. 44 is a graph comparing the performance of digital and analog flowmeters.

FIG. 45 is a flow chart showing operation of a self-validating meter.

FIG. 46 is a block diagram of a two-wire digital mass flowmeter.

FIGS. 47A-47C are graphs showing response of the digital mass flowmeter under wet and empty conditions.

FIG. 48A is a chart showing results for batching from empty trials.

FIG. 48B is a diagram showing an experimental flow rig.

FIG. 49 is a graph showing mass-flow errors against drop in apparent density.

FIG. 50 is a graph showing residual mass-flow errors after applying corrections.

FIG. 5 i is a graph showing on-line response of the self-validating digital mass flowmeter to the onset of two-phase flow.

FIG. 52 is a block diagram of a digital controller implementing a neural network processor that may be used with the digital mass flowmeter.

FIG. 53 is a flow diagram showing the technique for implementing the neural network to predict the mass-flow error and generate an error correction factor to correct the mass-flow measurement signal when two-phase flow is detected.

FIG. 54 is a 3D graph showing damping changes under two-phase flow conditions.

FIG. 55 is a flow diagram illustrating the experimental flow rig.

FIG. 56 is a 3D graph showing true mass flow error under two-phase flow conditions.

FIG. 57 is a 3D graph showing corrected mass flow error under two-phase flow conditions.

FIG. 5 is a graph comparing the uncorrected mass flow measurement signal with the neural network corrected mass flow measurement signal.

FIG. 59 is a flow chart of a procedure for compensating for error under multi-phase flow conditions.

FIG. 60 is a block diagram of a digital controller implementing a neural network processor that may be used with the digital mass flowmeter for multiple-phase fluid flows.

FIG. 61 is a flow diagram showing the technique for implementing the neural network to predict the mass-flow error and generate an error correction factor to correct the mass-flow measurement signal when multiple-phase flows are expected and/or detected.

FIG. 62 is a graphical view of a test matrix for wellheads tested based on actual testing at various well pressures and as velocities.

FIG. 63 is a graphical view of raw density errors at various liquid void fraction percentages and of wells at various velocities and pressures.

FIG. 64 is a graphical view of raw mass flow errors at various liquid void fraction percentages and of wells at various velocities and pressures.

FIG. 65 is a graphical view of raw liquid void fraction errors of wells at various velocities and pressures.

FIG. 66 is a graphical view of raw volumetric flow errors for wells at various velocities and pressures.

FIG. 67 is a graphical view of corrected liquid void fractions of wells at various velocities and pressures.

FIG. 68 is a graphical view of corrected mixture volumetric flow of wells at various velocities and pressures.

FIG. 69 is a graphical view of corrected gas mass flow of wells at various velocities and pressures.

FIG. 70 is a graphical view of corrected gas cumulative probability of the digital flowmeter tested.

FIG. 71 is a graphical view of corrected liquid mass flow error of wells at various velocities and pressures.

FIG. 72 is a graphical view of corrected gas cumulative probability of the digital flowmeter tested.

DETAILED DESCRIPTION

Techniques are provided for accounting for the effects of multi-phase flow in, for example, a digital flowmeter. The multi-phase flow may be, for example, a two-phase flow or a three-phase flow. In general, a two-phase flow is a fluid that includes two phases or components. For example, a two-phase flow may include a phase that includes a non-gas fluid (such as a liquid) and a phase that includes a gas. A three-phase flow is a fluid that includes three phases. For example, a three-phase flow may be a fluid with a gas phase and two non-gas liquids. For example, a three-phase flow may include natural gas, oil, and water. A two-phase flow may include, for example, natural gas and oil.

Although the digital flowmeter continues to operate in the presence of a multi-phase fluid, any properties (e.g., the mass flow rate and density of the multi-phase fluid) determined by the digital flowmeter may be inaccurate because the determination of these properties using conventional techniques is generally based on an assumption that the fluid flowing through the flowmeter is single-phase. Thus, even though the fluid is not a single phase fluid, the flowmeter may continue to operate and generate apparent values of properties such as the mass flow rate and density of the multi-phase fluid. As described below with respect to FIGS. 59-72, in one implementation parameters such as mass flow rate and density of each of the phases of the multi-phase flow may be determined from the apparent mass flow rate and the apparent density of the multi-phase fluid, in particular, and as discussed in more detail below, in one implementation, one or more intermediate values, such as the liquid volume fraction and the volumetric flowrate or gas and non-gas Froude numbers, are determined from the apparent mass flow rate and apparent density of the multi-phase fluid and the intermediate value(s) may be corrected to account for the presence of multiple phases in the fluid using a neural network or other mapping. The mass flow rate and density of each phase of the multi-phase fluid may be determined from the corrected intermediate value(s). Using the intermediate value(s) rather than the mass flow rate and density of the multi-phase fluid may help to improve the accuracy of the mass flow rate and density of each phase of the multi-phase fluid.

Before the techniques are described starting with reference to FIG. 59, digital flowmeters are discussed with reference to FIGS. 1-39. Various techniques for accounting for the effects of multi-phase flow in, for example, a digital flowmeter are discussed starting with FIG. 40.

Referring to FIG. 1, a digital mass flowmeter 100 includes a digital controller 105, one or more motion sensors 110, one or more drivers 115, a conduit 120 (also referred to as a flowtube), and a temperature sensor 125. The digital controller 105 may be implemented using one or more of, for example, a processor, a field-programmable gate array, an ASIC, other programmable logic or gate arrays, or programmable logic with a processor core. The digital controller generates a measurement of mass flow through the conduit 120 based at least on signals received from the motion sensors 110. The digital controller also controls the drivers 115 to induce motion in the conduit 120. This motion is sensed by the motion sensors 110.

Mass flow through the conduit 120 is related to the motion induced in the conduit in response to a driving force supplied by the drivers 115. In particular, mass flow is related to the phase and frequency of the motion, as well as to the temperature of the conduit. The digital mass flowmeter also may provide a measurement of the density of material flowing through the conduit. The density is related to the frequency of the motion and the temperature of the conduit. Many of the described techniques are applicable to a densitometer that provides a measure of density rather than a measure of mass flow.

The temperature in the conduit, which is measured using the temperature sensor 125, affects certain properties of the conduit, such as its stiffness and dimensions. The digital controller compensates for these temperature effects. The temperature of the digital controller 105 affects, for example, the operating frequency of the digital controller. In general, the effects of controller temperature are sufficiently small to be considered negligible. However, in some instances, the digital controller may measure the controller temperature using a solid state device and may compensate for effects of the controller temperature.

A. Mechanical Design

In one implementation, as illustrated in FIGS. 2A and 2B, the conduit 120 is designed to be inserted in a pipeline (not shown) having a small section removed or reserved to make room for the conduit. The conduit 120 includes mounting flanges 12 for connection to the pipeline, and a central manifold block 16 supporting two parallel planar loops 18 and 20 that are oriented perpendicularly to the pipeline. An electromagnetic driver 46 and a sensor 48 are attached between each end of loops 18 and 20. Each of the two drivers 46 corresponds to a driver 115 of FIG. 1, while each of the two sensors 48 corresponds to a sensor 110 of FIG. 1.

The drivers 46 on opposite ends of the loops are energized with current of equal magnitude but opposite sign (i.e., currents that are 180 out-of-phase) to cause straight sections 26 of the loops 18, 20 to rotate about their co-planar perpendicular bisector 56, which intersects the tube at point P (FIG. 2B). Repeatedly reversing (e.g., controlling sinusoidally) the energizing current supplied to the drivers causes each straight section 26 to undergo oscillatory motion that sweeps out a bow tie shape in the horizontal plane about line 56-56, the axis of symmetry of the loop. The entire lateral excursion of the loops at the lower rounded turns 38 and 40 is small, on the order of 1/16 of an inch for a two foot long straight section 26 of a pipe having a one inch diameter. The frequency of oscillation is typically about 80 to 90 Hertz,

B. Conduit Motion

The motion of the straight sections of loops 18 and 20 are shown in three modes in FIGS. 3A, 3B and 3C. In the drive mode shown in FIG. 3B, the loops are driven 180° out-of-phase about their respective points P so that the two loops rotate synchronously but in the opposite sense. Consequently, respective ends such as A and C periodically come together and go apart.

The drive motion shown in FIG. 3B induces the Coriolis mode motion shown in FIG. 3A, which is in opposite directions between the loops and moves the straight sections 26 slightly toward (or away) from each other. The Coriolis effect is directly related to mvW, where in is the mass of material in a cross section of a loop, v is the velocity at which the mass is moving (the volumetric flow rate), W is the angular velocity of the loop (W=W_(o) sin αt), and mv is the mass flow rate. The Coriolis effect is greatest when the two straight sections are driven sinusoidally and have a sinusoidally varying angular velocity. Under these conditions, the Coriolis effect is 90° out-of-phase with the drive signal.

FIG. 3C shows an undesirable common mode motion that deflects the loops in the same direction. This type of motion might be produced by an axial vibration in the pipeline in the example of FIGS. 2A and 2B because the loops are perpendicular to the pipeline.

The type of oscillation shown in FIG. 3B is called the antisymmetrical mode, and the Coriolis mode of FIG. 3A is called the symmetrical mode. The natural frequency of oscillation in the antisymmetrical mode is a function of the torsional resilience of the legs. Ordinarily the resonant frequency of the antisymmetrical mode for conduits of the shape shown in FIGS. 2A and 2B is higher than the resonant frequency of the symmetrical mode. To reduce the noise sensitivity of the mass flow measurement, it is desirable to maximize the Coriolis force for a given mass flow rate. As noted above, the loops are driven at their resonant frequency, and the Coriolis force is directly related to the frequency at which the loops are oscillating (i.e., the angular velocity of the loops). Accordingly, the loops are driven in the antisymmetrical mode, which tends to have the higher resonant frequency.

Other implementations may include different conduit designs. For example, a single loop or a straight tube section may be employed as the conduit.

C. Electronic Design

The digital controller 105 determines the mass flow rate by processing signals produced by the sensors 48 (i.e., the motion sensors 110) located at opposite ends of the loops. The signal produced by each sensor includes a component corresponding to the relative velocity at which the loops are driven by a driver positioned next to the sensor and a component corresponding to the relative velocity of the loops due to Coriolis forces induced in the loops. The loops are driven in the antisymmetrical mode, so that the components of the sensor signals corresponding to drive velocity are equal in magnitude but opposite in sign. The resulting Coriolis force is in the symmetrical mode so that the components of the sensor signals corresponding to Coriolis velocity are equal in magnitude and sign. Thus, differencing the signals cancels out the Coriolis velocity components and results in a difference that is proportional to the drive velocity. Similarly, summing the signals cancels out the drive velocity components and results in a sum that is proportional to the Coriolis velocity, which, in turn, is proportional to the Coriolis force. This sum then may be used to determine the mass flow rate.

1. Analog Control System

The digital mass flowmeter 100 provides considerable advantages over traditional, analog mass flowmeters. For use in later discussion. FIG. 4 illustrates an analog control system 400 of a traditional mass flowmeter. The sensors 4 each produce a voltage signal, with signal V_(A0) being produced by sensor 48 a and signal V_(B0) being produced by sensor 48 b. V_(A0) and V_(B0) to correspond to the velocity of the loops relative to each other at the positions of the sensors. Prior to processing, signals V_(A0) and V_(B0) are amplified at respective input amplifiers 405 and 410 to produce signals V_(A1) and V_(B1). To correct for imbalances in the amplifiers and the sensors, input amplifier 410 has a variable gain that is controlled by a balance signal coming from a feedback loop that contains a synchronous demodulator 415 and an integrator 420.

At the output of amplifier 405, signal V_(A1) is of the form:

V _(A1) =V _(D) sin ωt+V _(C) cos ωt,

and, at the output of amplifier 410, signal V_(B1) is of the form:

V _(B1) =−V _(D) sin ωt+V _(C) cos ωt,

where V_(D) and V_(C) are, respectively, the drive voltage and the Coriolis voltage, and ω is the drive mode angular frequency.

Voltages V_(A1) and V_(B1) are differenced by operational amplifier 425 to produce:

V _(DRV) =V _(A1) −V _(B1)=2V _(D) sin θt,

where V_(DRV) corresponds to the drive motion and is used to power the drivers. In addition to powering the drivers, V_(DRV) is supplied to a positive going zero crossing detector 430 that produces an output square wave F_(DRV) having a frequency corresponding to that of V_(DRV) (ω=2πF_(DRV)). F_(DRV) is used as the input to a digital phase locked loop circuit 435. F_(DRV) also is supplied to a processor 440.

Voltages V_(A1) and V_(B1) are summed by operational amplifier 445 to produce:

V _(COR) =V _(A1) +V _(B1)=2V _(C) cos ωt,

where V_(COR) is related to the induced Coriolis motion.

V_(COR) is supplied to a synchronous demodulator 450 that produces an output voltage V_(M) that is directly proportional to mass by rejecting the components of V_(COR) that do not have the same frequency as, and are not in phase with, a gating signal Q. The phase locked loop circuit 435 produces Q, which is a quadrature reference signal that has the same frequency (α) as V_(DRV) and is 90 out of phase with V_(DRV) (i.e., in phase with V_(COR)). Accordingly, synchronous demodulator 450 rejects frequencies other than a so that V_(M) corresponds to the amplitude of V_(COR) at α. This amplitude is directly proportional to the mass in the conduit.

V_(M) is supplied to a voltage-to-frequency converter 455 that produces a square wave signal F_(M) having a frequency that corresponds to the amplitude of V_(M). The processor 440 then divides F_(M) by F_(DRV) to produce a measurement of the mass flow rate.

Digital phase locked loop circuit 435 also produces a reference signal I that is in phase with V_(DRV) and is used to gate the synchronous demodulator 415 in the feedback loop controlling amplifier 410. When the gains of the input amplifiers 405 and 410 multiplied by the drive components of the corresponding input signals are equal, the summing operation at operational amplifier 445 produces zero drive component (i.e., no signal in phase with V_(DRV)) in the signal V_(CDR). When the gains of the input amplifiers 405 and 410 are not equal, a drive component exists in V_(COR). This drive component is extracted by synchronous demodulator 415 and integrated by integrator 420 to generate an error voltage that corrects the gain of input amplifier 410. When the gain is too high or too low, the synchronous demodulator 415 produces an output voltage that causes the integrator to change the error voltage that modifies the gain. When the gain reaches the desired value, the output of the synchronous modulator goes to zero and the error voltage stops changing to maintain the gain at the desired value.

2. Digital Control System

FIG. 5 provides a block diagram of an implementation 500 of the digital mass flowmeter 100 that includes the conduit 120, drivers 46, and sensors 48 of FIGS. 2A and 2B, along with a digital controller 505. Analog signals from the sensors 48 are converted to digital signals by analog-to-digital (“A/D”) converters 510 and supplied to the controller 505. The A/D converters may be implemented as separate converters, or as separate channels of a single converter.

Digital-to-analog (“D/A”) converters 515 convert digital control signals from the controller 505 to analog signals for driving the drivers 46. The use of a separate drive signal for each driver has a number of advantages. For example, the system may easily switch between symmetrical and antisymmetrical drive modes for diagnostic purposes. In other implementations, the signals produced by converters 515 may be amplified by amplifiers prior to being supplied to the drivers 46. In still other implementations, a single D/A converter may be used to produce a drive signal applied to both drivers, with the drive signal being inverted prior to being provided to one of the drivers to drive the conduit 120 in the antisymmetrical mode.

High precision resistors 520 and amplifiers 525 are used to measure the current supplied to each driver 46. A/D converters 530 convert the measured current to digital signals and supply the digital signals to controller 505. The controller 505 uses the measured currents in generating the driving signals.

Temperature sensors 535 and pressure sensors 540 measure, respectively, the temperature and the pressure at the inlet 545 and the outlet 550 of the conduit. A/D converters 555 convert the measured values to digital signals and supply the digital signals to the controller 505. The controller 505 uses the measured values in a number of ways. For example, the difference between the pressure measurements may be used to determine a back pressure in the conduit. Since the stiffness of the conduit varies with the back pressure, the controller may account for conduit stiffness based on the determined back pressure.

An additional temperature sensor 560 measures the temperature of the crystal oscillator 565 used by the A/D converters. An A/D converter 570 converts this temperature measurement to a digital signal for use by the controller 505. The input/output relationship of the A/D converters varies with the operating frequency of the converters, and the operating frequency varies with the temperature of the crystal oscillator. Accordingly, the controller uses the temperature measurement to adjust the data provided by the A/D converters, or in system calibration.

In the implementation of FIG. 5, the digital controller 505 processes the digitized sensor signals produced by the A/D converters 510 according to the procedure 600 illustrated in FIG. 6 to generate the mass flow measurement and the drive signal supplied to the drivers 46. Initially, the controller collects data from the sensors (step 605). Using this data, the controller determines the frequency of the sensor signals (step 610), eliminates zero offset from the sensor signals (step 615), and determines the amplitude (step 620) and phase (step 625) of the sensor signals. The controller uses these calculated values to generate the drive signal (step 630) and to generate the mass flow and other measurements (step 635). After generating the drive signals and measurements, the controller collects a new set of data and repeats the procedure. The steps of the procedure 600 may be performed serially or in parallel, and may be performed in varying order.

Because of the relationships between frequency, zero offset, amplitude, and phase, an estimate of one may be used in calculating another. This leads to repeated calculations to improve accuracy. For example, an initial frequency determination used in determining the zero offset in the sensor signals may be revised using offset-eliminated sensor signals. In addition, where appropriate, values generated for a cycle may be used as starting estimates for a following cycle.

a. Data Collection

For ease of discussion, the digitized signals from the two sensors will be referred to as signals SV₁ and SV₂, with signal SV₁ coming from sensor 48 a and signal SV₂ coming from sensor 48 b. Although new data is generated constantly, it is assumed that calculations are based upon data corresponding to one complete cycle of both sensors. With sufficient data buffering, this condition will be true so long as the average time to process data is less than the time taken to collect the data. Tasks to be carried out for a cycle include deciding that the cycle has been completed, calculating the frequency of the cycle for the frequencies of SV_(E) and SV₂), calculating the amplitudes of SV₁ and SV₂, and calculating the phase difference between SV₁ and SV₂. In some implementations, these calculations are repeated for each cycle using the end point of the previous cycle as the start for the next. In other implementations, the cycles overlap by 80° or other amounts (e.g., 90°) so that a cycle is subsumed within the cycles that precede and follow it.

FIGS. 7A and 7B illustrate two vectors of sampled data from signals SV₁ and SV₂, which are named, respectively, sv1_in and sv2_in. The first sampling point of each vector is known, and corresponds to a zero crossing of the sine wave represented by the vector. For sv1_in, the first sampling point is the zero crossing from a negative value to a positive value, while for sv2_in the first sampling point is the zero crossing from a positive value to a negative value.

An actual starting point for a cycle (i.e., the actual zero crossing) will rarely coincide exactly with a sampling point. For this reason, the initial sampling points (start_sample_SV1 and start_sample_SV2) are the sampling points occurring just before the start of the cycle. To account for the difference between the first sampling point and the actual start of the cycle, the approach also uses the position (start_offset_SV1 or start_offset_SV2) between the starting sample and the next sample at which the cycle actually begins.

Since there is a phase offset between signals SV₁ and SV₂, sv1_in and sv2_in may start at different sampling points. If both the sample rate and the phase difference are high, there may be a difference of several samples between the start of sv1_in and the start of sv2_in. This difference provides a crude estimate of the phase offset, and may be used as a check on the calculated phase offset, which is discussed below. For example, when sampling at 55 kHz, one sample corresponds to approximately 0.5 degrees of phase shift, and one cycle corresponds to about 800 sample points.

When the controller employs functions such as the sum (A+B) and difference (A−B), with B weighted to have the same amplitude as A, additional variables (e.g., start_sample_sum and start_offset_sum) track the start of the period for each function. The sum and difference functions have a phase offset halfway between SV₁ and SV₂.

In one implementation, the data structure employed to store the data from the sensors is a circular list for each sensor, with a capacity of at least twice the maximum number of samples in a cycle. With this data structure, processing may be carried out on data for a current cycle while interrupts or other techniques are used to add data for a following cycle to the lists.

Processing is performed on data corresponding to a full cycle to avoid errors when employing approaches based on sine-waves. Accordingly, the first task in assembling data for a cycle is to determine where the cycle begins and ends. When nonoverlapping cycles are employed, the beginning of the cycle may be identified as the end of the previous cycle. When overlapping cycles are employed, and the cycles overlap by 180°, the beginning of the cycle may be identified as the midpoint of the previous cycle, or as the endpoint of the cycle preceding the previous cycle.

The end of the cycle may be first estimated based on the parameters of the previous cycle and under the assumption that the parameters will not change by more than a predetermined amount from cycle to cycle. For example, five percent may be used as the maximum permitted change from the last cycle's value, which is reasonable since, at sampling rates of 55 kHz, repeated increases or decreases of five percent in amplitude or frequency over consecutive cycles would result in changes of dose to 5,000 percent in one second.

By designating five percent as the maximum permissible increase in amplitude and frequency, and allowing for a maximum phase change of 5° in consecutive cycles, a conservative estimate for the upper limit on the end of the cycle for signal SV₁ may be determined as:

${{end\_ sample}{\_ SV1}} \leq {{{start\_ sample}{\_ SV1}} + {\frac{365}{360}*\frac{sample\_ rate}{{est\_ freq}*0.95}}}$

where start_sample_SV1 is the first sample of sv1_in, sample rate is the sampling rate, and est_freq is the frequency from the previous cycle. The upper limit on the end of the cycle for signal SV₂ (end_sample_SV2) may be determined similarly.

After the end of a cycle is identified, simple checks may be made as to whether the cycle is worth processing. A cycle may not be worth processing when, for example, the conduit has stalled or the sensor waveforms are severely distorted. Processing only suitable cycles provides considerable reductions in computation.

One way to determine cycle suitability is to examine certain points of a cycle to confirm expected behavior. As noted above, the amplitudes and frequency of the last cycle give useful starting estimates of the corresponding values for the current cycle. Using these values, the points corresponding to 30°, 150°, 210° and 330° of the cycle may be examined. If the amplitude and frequency were to match exactly the amplitude and frequency for the previous cycle, these points should have values corresponding to est_amp/2, est_amp/2, −est_amp/2, and −est_amp/2, respectively, where est_amp is the estimated amplitude of a signal (i.e., the amplitude from the previous cycle). Allowing for a five percent change in both amplitude and frequency, inequalities may be generated for each quarter cycle. For the 30° point, the inequality is

${{sv1\_ in}\left( {{{start\_ sample}{\_ SV1}} + {\frac{30}{360}*\frac{sample\_ rate}{{est\_ freq}*1.05}}} \right)} > {0.475*{est\_ amp}{\_ SV}}$

The inequalities for the other points have the same form, with the degree offset term (x/360) and the sign of the est_amp_SV1 term having appropriate values. These inequalities can be used to check that the conduit has vibrated in a reasonable manner.

Measurement processing takes place on the vectors sv1_in(start:end) and sv2_in(start:end) where:

start=min(start_sample_(—) SV1,start_sample_(—) SV2), and

end=max(end_sample_(—) SV1,end_sample_(—) SV2),

The difference between the start and end points for a signal is indicative of the frequency of the signal.

b. Frequency Determination

The frequency of a discretely-sampled pure sine wave may be calculated by detecting the transition between periods (i.e., by detecting positive or negative zero-crossings) and counting the number of samples in each period. Using this method, sampling, for example, an 82.2 Hz sine wave at 55 kHz will provide an estimate of frequency with a maximum error of 0.15 percent. Greater accuracy may be achieved by estimating the fractional part of a sample at which the zero-crossing actually occurred using, for example, start_offset_SV1 and start_offset_SV2. Random noise and zero offset may reduce the accuracy of this approach.

As illustrated in FIGS. 8A and 8B, a more refined method of frequency determination uses quadratic interpolation of the square of the sine wave. With this method, the square of the sine wave is calculated, a quadratic function is fitted to match the minimum point of the squared sine wave, and the zeros of the quadratic function are used to determine the frequency. If

sv _(t) =A sin x _(t)+δ+σε_(t),

where sv_(t) is the sensor voltage at time t, A is the amplitude of oscillation, x_(t) is the radian angle at time t (i.e., x_(t)=2πft), Λ is the zero offset, M₁ is a random variable with distribution N(0,1), and [ is the variance of the noise, then the squared function is given by:

sv _(t) ² =A ² sin² x _(t)+2A(δ+σε_(t))sin x _(t)+2δσε_(t)+δ²+σ²ε_(t) ².

When x₁ is close to 2π, sin x_(t) and sin² x_(t) can be approximated as x_(0t)=x_(t)−2Ξ and x_(0t) ², respectively. Accordingly, for values of x_(t) close to 2Ξ, a_(t) can be approximated as:

a_(t)² ≈ A²x_(0 t)² + 2A(δ + σɛ_(t))x_(0t) + 2δ σ ɛ_(t) + δ² + σ²ɛ_(t)² ≈ (A²x_(0t)² + 2A δ x_(0t) + δ²) + σ ɛ_(t)(2A x_(0t) + 2δ + σ ɛ_(t)).

This is a pure quadratic (with a non-zero minimum, assuming Θ=0) plus noise, with the amplitude of the noise being dependent upon both [ and Λ. Linear interpolation also could be used.

Error sources associated with this curve fitting technique are random noise, zero offset, and deviation from a true quadratic. Curve fitting is highly sensitive to the level of random noise. Zero offset in the sensor voltage increases the amplitude of noise in the sine-squared function, and illustrates the importance of zero offset elimination (discussed below). Moving away from the minimum, the square of even a pure sine wave is not entirely quadratic. The most significant extra term is of fourth order. By contrast, the most significant extra term for linear interpolation is of third order.

Degrees of freedom associated with this curve fitting technique are related to how many, and which, data points are used. The minimum is three, but more may be used (at greater computational expense) by using least-squares fitting. Such a fit is less susceptible to random noise. FIG. 8A illustrates that a quadratic approximation is good up to some 20° away from the minimum point. Using data points further away from the minimum will reduce the influence of random noise, but will increase the errors due to the non-quadratic terms (i.e., fourth order and higher) in the sine-squared function.

FIG. 9 illustrates a procedure 900 for performing the curve fitting technique. As a first step, the controller initializes variables (step 905). These variables include end_point, the best estimate of the zero crossing point; ep_int, the integer value nearest to end_point; s[0 . . . i], the set of all sample points; z[k], the square of the sample point closest to end_point; z[0 . . . n−1], a set of squared sample points used to calculate end_point; n, the number of sample points used to calculate end_point (n=2k+1); step_length, the number of samples in between consecutive values in z; and iteration_count, a count of the iterations that the controller has performed.

The controller then generates a first estimate of end_point (step 910). The controller generates this estimate by calculating an estimated zero-crossing point based on the estimated frequency from the previous cycle and searching around the estimated crossing point (forwards and backwards) to find the nearest true crossing point (i.e., the occurrence of consecutive samples with different signs). The controller then sets end_point equal to the sample point having the smaller magnitude of the samples surrounding the true crossing point.

Next, the controller sets n, the number of points for curve fitting (step 915). The controller sets n equal to 5 for a sample rate of 11 kHz, and to 21 for a sample rate of 44 kHz. The controller then sets iteration_count to 0 (step 920) and increments iteration_count (step 925) to begin the iterative portion of the procedure.

As a first step in the iterative portion of the procedure, the controller selects step_length (step 930) based on the value of iteration_count. The controller sets step_length equal to 6, 3, or 1 depending on whether iteration_count equals, respectively, 1, 2 or 3.

Next, the controller determines ep_int as the integer portion of the sum of end_point and 0.5 (step 935) and fills the z array (step 940). For example, when n equals 5, z[0] s[ep_int−2*step_length]², z[1]=s[ep_int−step_length]², z[1]=s[ep_int]², z[3]=s[ep_int+step_length]², and z[4]=s[ep_int+2*step_length]².

Next, the controller uses a filter, such as a Savitzky-Golay filter, to calculate smoothed values of z[k−1], z[k] and z[k+1] (step 945). Savitzky-Golay smoothing filters are discussed by Press et al. in Numerical Recipes in C, pp, 650-655 (2nd ed., Cambridge University Press, 1995), which is incorporated by reference. The controller then its a quadratic to z[k−1], z[k] and z[k+1] (step 950), and calculates the minimum value of the quadratic (z*) and the corresponding position (x*) (step 955).

If x* is between the points corresponding to k−1 and k+1 (step 960), then the controller sets end_point equal to x* (step 965). Thereafter, if iteration_count is less than 3 (step 970), the controller increments iteration_count (step 925) and repeats the iterative portion of the procedure.

If x* is not between the points corresponding to k−1 and k+1 (step 960), or if iteration_count equals 3 (step 970), the controller exits the iterative portion of the procedure. The controller then calculates the frequency based on the difference between end_point and the starting point for the cycle, which is known (step 975).

In essence, the procedure 900 causes the controller to make three attempts to home in on end_point, using smaller step_lengths in each attempt. If the resulting minimum for any attempt falls outside of the points used to fit the curve (i.e., there has been extrapolation rather than interpolation), this indicates that either the previous or new estimate is poor, and that a reduction in step size is unwarranted.

The procedure 900 may be applied to at least three different sine waves produced by the sensors. These include signals SV₁ and SV₂ and the weighted sum of the two. Moreover, assuming that zero offset is eliminated, the frequency estimates produced for these signals are independent. This is clearly true for signals SV₁ and SV₂, as the errors on each are independent. It is also true, however, for the weighted sum, as long as the mass flow and the corresponding phase difference between signals SV₁ and SV₂ are large enough for the calculation of frequency to be based on different samples in each case. When this is true, the random errors in the frequency estimates also should be independent.

The three independent estimates of frequency can be combined to provide an improved estimate. This combined estimate is simply the mean of the three frequency estimates.

c. Zero Offset Compensation

An important error source in a Coriolis transmitter is zero offset in each of the sensor voltages. Zero offset is introduced into a sensor voltage signal by drift in the pre-amplification circuitry and the analog-to-digital converter. The zero offset effect may be worsened by slight differences in the pre-amplification gains for positive and negative voltages due to the use of differential circuitry. Each error source varies between transmitters, and will vary with transmitter temperature and more generally over time with component wear.

An example of the zero offset compensation technique employed by the controller is discussed in detail below. In general, the controller uses the frequency estimate and an integration technique to determine the zero offset in each of the sensor signals. The controller then eliminates the zero offset from those signals. After eliminating zero offset from signals SV₁ and SV₂, the controller may recalculate the frequency of those signals to provide an improved estimate of the frequency.

d. Amplitude Determination

The amplitude of oscillation has a variety of potential uses. These include regulating conduit oscillation via feedback, balancing contributions of sensor voltages when synthesizing driver waveforms, calculating sums and differences for phase measurement, and calculating an amplitude rate of change for measurement correction purposes.

In one implementation, the controller uses the estimated amplitudes of signals SV₁ and SV₂ to calculate the sum and difference of signals SV₁ and SV₂, and the product of the sum and difference. Prior to determining the sum and difference, the controller compensates one of the signals to account for differences between the gains of the two sensors. For example, the controller may compensate the data for signal SV₂ based on the ratio of the amplitude of signal SV₁ to the amplitude of signal SV₂ so that both signals have the same amplitude.

The controller may produce an additional estimate of the frequency based on the calculated sum. This estimate may be averaged with previous frequency estimates to produce a refined estimate of the frequency of the signals, or may replace the previous estimates.

The controller may calculate the amplitude according to a Fourier-based technique to eliminate the effects of higher harmonics. A sensor voltage x(t) over a period T (as identified using zero crossing techniques) can be represented by an offset and a series of harmonic terms as:

x(t)=a ₀/2+a ₁ cos(αt)+a ₂ cos(2αt)+a ₃ cos(3αt)+ . . . +b ₁ sin(αt)+b ₂ sin(2αt)+ . . .

With this representation, a non-zero offset a₀ will result in non-zero cosine terms a_(n). Though the amplitude of interest is the amplitude of the fundamental component (i.e., the amplitude at frequency ω), monitoring the amplitudes of higher harmonic components (i.e., at frequencies lω, where k is greater than 1) may be of value for diagnostic purposes. The values of a_(n) and b_(n) may be calculated as:

${a_{n} = {\frac{2}{T}{\int_{0}^{T}{{x(t)}\cos \; n\; \omega \; {t}}}}},$

and

$b_{n} = {\frac{2}{T}{\int_{0}^{T}{{x(t)}\sin \; n\; \omega \ {{t}.}}}}$

The amplitude, A_(n), of each harmonic is given by:

A _(n)=√{square root over (a _(n) ² +b _(n) ²)}.

The integrals are calculated using Simpson's method with quadratic correction (described below). The chief computational expense of the method is calculating the pure sine and cosine functions.

e. Phase Determination

The controller may use a number of approaches to calculate the phase difference between signals SV₁ and SV₂. For example, the controller may determine the phase offset of each harmonic, relative to the starting time at t=0, as:

$\phi_{n} = {\tan^{- 1}{\frac{a_{n}}{b_{n}}.}}$

The phase offset is interpreted in the context of a single waveform as being the difference between the start of the cycle (i.e., the zero-crossing point) and the point of zero phase for the component of SV(t) of frequency e). Since the phase offset is an average over the entire waveform, it may be used as the phase offset from the midpoint of the cycle. Ideally, with no zero offset and constant amplitude of oscillation, the phase offset should be zero every cycle. The controller may determine the phase difference by comparing the phase offset of each sensor voltage over the same time period.

The amplitude and phase may be generated using a Fourier method that eliminates the effects of higher harmonics. This method has the advantage that it does not assume thin both ends of the conduits are oscillating at the same frequency. As a first step in the method, a frequency estimate is produced using the zero crossings to measure the time between the start and end of the cycle. If linear variation in frequency is assumed, this estimate equals the time-averaged frequency over the period. Using the estimated, and assumed time-invariant, frequency ω of the cycle, the controller calculates:

${I_{1} = {\frac{2\omega}{\pi}{\int_{0}^{\frac{2\pi}{\omega}}{{{SV}(t)}{\sin \left( {\omega \mspace{11mu} t} \right)}{t}}}}},$

and

${I_{2} = {\frac{2\omega}{\pi}{\int_{0}^{\frac{2\pi}{\omega}}{S\; {V(t)}{\cos \left( {\omega \; t} \right)}\ {t}}}}},$

where SV(t) is the sensor voltage waveform (i.e., SV₁(t) or SV₂(t)). The controller then determines the estimates of the amplitude and phase:

Amp=√{square root over (I ₁ ² +I ₂ ²)}, and

${Phase} = {\tan^{- 1}{\frac{I_{2}}{I_{1}}.}}$

The controller then calculates a phase difference, assuming that the average phase and frequency of each sensor signal is representative of the entire waveform. Since these frequencies are different for SV₁ and SV₂, the corresponding phases are scaled to the average frequency. In addition, the phases are shifted to the same starting point (i.e., the midpoint of the cycle on SV₁). After scaling, they are subtracted to provide the phase difference:

$\mspace{20mu} {{{{scaled\_ phase}{\_ SV}_{1}} = {{phase\_ SV}_{1}\; \frac{av\_ freq}{{freq\_ SV}_{1}}}},{{{scaled\_ shift}{\_ SV}_{2}} = \frac{\left( {{midpoint\_ SV}_{2} - {midpoint\_ SV}_{1}} \right){hfreq\_ SV}_{2}}{360}},}$

and

${{{scaled\_ phase}{\_ SV}_{2}} = {\left( {{phase\_ SV}_{2} + {{scale\_ shift}{\_ SV}_{2}}} \right)\frac{av\_ freq}{{freq\_ SV}_{2}}}},$

where h is the sample length and the midpoints are defined in terms of samples:

${midpoint\_ SV}_{x} = \frac{\left( {{startpoint\_ SV}_{x} + {endpoint\_ SV}_{x}} \right)}{2}$

In general, phase and amplitude are not calculated over the same time-frame for the two sensors. When the flow rate is zero, the two cycle mid-points are coincident. However, they diverge at high flow rates so that the calculations are based on sample sets that are not coincident in time. This leads to increased phase noise in conditions of changing mass flow. At full flow rate, a phase shift of 4° (out of 360°) means that only 99% of the samples in the SV₁ and SV₂ data sets are coincident. Far greater phase shifts may be observed under aerated conditions, which may lead to even lower rates of overlap.

FIG. 10 illustrates a modified approach 1000 that addresses this issue. First, the controller finds the frequencies (f₁, f₂) and the mid-points (m₁, m₂) of the SV₁ and SV₂ data sets (d₁, d₂) (step 1005). Assuming linear shift in frequency from the last cycle, the controller calculates the frequency of SV₂ at the midpoint of SV₁ (f_(2m1)) and the frequency of SV₁ at the midpoint of SV₂ (f_(1m2)) (step 1010).

The controller then calculates the starting and ending points of new data sets (d_(1m2) and d_(2m1)) with mid-points m₂ and m₁ respectively, and assuming frequencies of f_(1m2) and f_(2m1) (step 1015). These end points do not necessarily coincide with zero crossing points. However, this is not a requirement for Fourier-based calculations.

The controller then carries out the Fourier calculations of phase and amplitude on the sets d₁ and d_(2m1), and the phase difference calculations outlined above (step 1020). Since the mid-points of d₁ and d_(2m1) are identical, scale-shift_SV₂ is always zero and can be ignored. The controller repeats these calculations for the data sets d₂ and d_(1m2) (step 1025). The controller then generates averages of the calculated amplitude and phase difference for use in measurement generation (step 1030). When there is sufficient separation between the mid points m₁ and m₂, the controller also may use the two sets of results to provide local estimates of the rates of change of phase and amplitude.

The controller also may use a difference-amplitude method that involves calculating a difference between SV₁ and SV₂, squaring the calculated difference, and integrating the result. According to another approach, the controller synthesizes a sine wave, multiplies the sine wave by the difference between signals SV₁ and SV₂, and integrates the result. The controller also may integrate the product of signals SV₁ and SV₂, which is a sine wave having a frequency 2f (where f is the average frequency of signals SV₁ and SV₂), or may square the product and integrate the result. The controller also may synthesize a cosine wave comparable to the product sine wave and multiply the synthesized cosine wave by the product sine wave to produce a sine wave of frequency 4f that the controller then integrates. The controller also may use multiple ones of these approaches to produce separate phase measurements, and then may calculate a mean value of the separate measurements as the final phase measurement.

The difference-amplitude method starts with:

${{{SV}_{1}(t)} = {{A_{1}{\sin \left( {{2\pi \; {ft}} + \frac{\phi}{2}} \right)}\mspace{14mu} {and}\mspace{14mu} {{SV}_{2}(t)}} = {A_{2}{\sin \left( {{2\pi \; {ft}} - \frac{\phi}{2}} \right)}}}},$

to where ⊥ is the phase difference between the sensors. Basic trigonometric identities may be used to define the sum (Sum) and difference (Diff) between the signals as:

${{{Sum} \equiv {{{SV}_{1}(t)} + {\frac{A_{1}}{A_{2}}{{SV}_{2}(t)}}}} = {2A_{1}\cos \; {\frac{\phi}{2} \cdot \sin}\; 2\; \pi \; f\; t}},$

and

${{Diff} \equiv {{{SV}_{1}(t)} - {\frac{A_{1}}{A_{2}}{{SV}_{2}(t)}}}} = {2\; A_{1}\sin {\frac{\phi}{2} \cdot \cos}\; 2\pi \; {{ft}.}}$

These functions have amplitudes of 2A₁ cos(⊥2) and 2A₁ sin(⊥2), respectively. The controller calculates data sets for Sum and Diff from the data for SV₁ and SV₂, and then uses one or more of the methods described above to calculate the amplitude of the signals represented by those data sets. The controller then uses the calculated amplitudes to calculate the phase difference, ⊥.

As an alternative, the phase difference may be calculated using the function Prod, defined as:

$\begin{matrix} {{Prod} \equiv {{Sum} \times {Diff}}} \\ {= {4A_{1}^{2}\cos \; {\frac{\phi}{2} \cdot \sin}\; {\frac{\phi}{2} \cdot \cos}\; 2\; \pi \; f\; {t \cdot \sin}\; 2\; \pi \; f\; t}} \\ {{= {A_{1}^{2}\sin \; {\phi \cdot \sin}\; 4\pi \; f\; t}},} \end{matrix}$

which is a function with amplitude A² sin ⊥ and frequency 2f. Prod can be generated sample by sample, and ⊥ may be calculated from the amplitude of the resulting sine wave. The calculation of phase is particularly dependent upon the accuracy of previous calculations (i.e., the calculation of the frequencies and amplitudes of SV₁ and SV₂). The controller may use multiple methods to provide separate (if not entirely independent) estimates of the phase, which may be combined to give an improved estimate.

f. Drive Signal Generation

The controller generates the drive signal by applying a gain to the difference between signals SV₁ and SV₂. The controller may apply either a positive gain (resulting in positive feedback) or a negative gain (resulting in negative feedback).

In general, the Q of the conduit is high enough that the conduit will resonate only at certain discrete frequencies. For example, the lowest resonant frequency for some conduits is between 65 Hz and 95 Hz, depending on the density of the process fluid, and irrespective of the drive frequency. As such, it is desirable to drive the conduit at the resonant frequency to minimize cycle-to-cycle energy loss. Feeding back the sensor voltage to the drivers permits the drive frequency to migrate to the resonant frequency.

As an alternative to using feedback to generate the drive signal, pure sine waves having phases and frequencies determined as described above may be synthesized and sent to the drivers. This approach offers the advantage of eliminating undesirable high frequency components, such as harmonics of the resonant frequency. This approach also permits compensation for time delays introduced by the analog-to-digital converters, processing, and digital-to-analog converters to ensure that the phase of the drive signal corresponds to the mid-point of the phases of the sensor signals. This compensation may be provided by determining the time delay of the system components and introducing a phase shift corresponding to the time delay.

Another approach to driving the conduit is to use square wave pulses. This is another synthesis method, with fixed (positive and negative) direct current sources being switched on and off at timed intervals to provide the required energy. The switching is synchronized with the sensor voltage phase. Advantageously, this approach does not require digital-to-analog converters.

In general, the amplitude of vibration of the conduit should rapidly achieve a desired value at startup, so as to quickly provide the measurement function, but should do so without significant overshoot, which may damage the meter. The desired rapid startup may be achieved by setting a very high gain so that the presence of random noise and the high Q of the conduit are sufficient to initiate motion of the conduit. In one implementation, high gain and positive feedback are used to initiate motion of the conduit. Once stable operation is attained, the system switches to a synthesis approach for generating the drive signals.

Referring to FIGS. 11A-13D, synthesis methods also may be used to initiate conduit motion when high gain is unable to do so. For example, if the DC voltage offset of the sensor voltages is significantly larger than random noise, the application of a high gain will not induce oscillatory motion. This condition is shown in FIGS. 11A-11D, in which a high gain is applied at approximately 0.3 seconds. As shown in FIGS. 11A and 11B, application of the high gain causes one of the drive signals to assume a large positive value (FIG. 11A) and the other to assume a large negative value (FIG. 11B). The magnitudes of the drive signals vary with noise in the sensor signals (FIGS. 11C and 11D). However, the amplified noise is insufficient to vary the sign of the drive signals so as to induce oscillation.

FIGS. 12A-12D illustrate that imposition of a square wave over several cycles can reliably cause a rapid startup of oscillation. Oscillation of a conduit having a two inch diameter may be established in approximately two seconds. The establishment of conduit oscillation is indicated by the reduction in the amplitude of the drive signals, as shown in FIGS. 12A and 12B. FIGS. 13A-13D illustrate that oscillation of a one inch conduit may be established in approximately half a second.

A square wave also may be used during operation to correct conduit osculation problems. For example, in some circumstances, flow meter conduits have been known to begin oscillating at harmonics of the resonant frequency of the conduit, such as frequencies on the order of 1.5 kHz. When such high frequency oscillations are detected, a square wave having a more desirable frequency may be used to return the conduit oscillation to the resonant frequency.

g. Measurement Generation

The controller digitally generates the mass flow measurement in a manner similar to the approach used by the analog controller. The controller also may generate other measurements, such as density.

In one implementation, the controller calculates the mass flow based on the phase difference in degrees between the two sensor signals (phase_diff), the frequency of oscillation of the conduit (freq), and the process temperature (temp):

T _(z)=temp−T _(c),

noneu _(—) mf=tan(Ξ*phase_diff/180), and

massflow=16(MF ₁ *T _(z) ² +MF ₂ *T _(z) +MF ₃)*noneu _(—) mf/freq,

where T_(c) is a calibration temperature, MF₁-MF₃ are calibration constants calculated during a calibration procedure, and noneu_mf is the mass flow in non-engineering units.

The controller calculates the density based on the frequency of oscillation of the conduit and the process temperature:

T _(z)=temp−T _(c),

c ₂=freq², and

density=(D ₁ *T _(z) ² +D ₂ *T _(z) +D ₃)/c ₂ +D ₄ *T _(z) ²,

where D₁-D₄ are calibration constants generated during a calibration procedure.

D. Integration Techniques

Many integration techniques are available, with different techniques requiring different levels of computational effort and providing different levels of accuracy. In the described implementation, variants of Simpson's method are used. The basic technique may be expressed as;

${{\int_{t_{n}}^{t_{n + 2}}{y{t}}} \approx {\frac{h}{3}\left( {y_{n} + {4y_{n + 1}} + y_{n + 2}} \right)}},$

where t_(k) is the time at sample k, is the corresponding function value, and h is the step length. This rule can be applied repeatedly to any data vector with an odd number of data points i.e., three or more points), and is equivalent to fitting and integrating a cubic spline to the data points. If the number of data points happens to be even, then the so-called ⅜ths rule can be applied at one end of the interval:

${\int_{t_{n}}^{t_{n + 3}}{y{t}}} \approx {\frac{3h}{8}{\left( {y_{n} + {3y_{n + 1}} + {3y_{n + 2}} + y_{n + 3}} \right).}}$

As stated earlier, each cycle begins and ends at some offset (e.g., start_offset_SV1) from a sampling point. The accuracy of the integration techniques are improved considerably by taking these offsets into account. For example, in an integration of a half cycle sine wave, the areas corresponding to partial samples must be included in the calculations to avoid a consistent underestimate in the result.

Two types of function are integrated in the described calculations: either sine or sine-squared functions. Both are easily approximated close to zero where the end points occur. At the end points, the sine wave is approximately linear and the sine-squared function is approximately quadratic.

In view of these two types of functions, three different integration methods have been evaluated. These are Simpson's method with no end correction, Simpson's method with linear end correction, and Simpson's method with quadratic correction.

The integration methods were tested by generating and sampling pure sine and sine-squared functions, without simulating any analog-to-digital truncation error. Integrals were calculated and the results were compared to the true amplitudes of the signals. The only source of error in these calculations was due to the integration techniques. The results obtained are illustrated in tables A and B.

TABLE A Integration of a sine function Av. S.D. Max. % error (based on 1000 simulations) Errors (%) Error (%) Error (%) Simpson Only −3.73e−3 1.33e−3 6.17e−3 Simpson + linear correction 3.16e−8 4.89e−8 1.58e−7 Simpson + quadratic correction 2.00e−4 2.19e−2 5.18e−1

TABLE B Integration of a sine-squared function Av. S.D. Max. % error (based on 1000 simulations Errors (%) Error (%) Error (%) Simpson Only −2.21e−6 1.10e−6 4.39e−3 Simpson + linear correction 2.21e−6 6.93e−7 2.52e−6 Simpson + quadratic correction 2.15e−11 6.83e−11 1.88e−10

For sine functions, Simpson's method with linear correction was unbiased with the smallest standard deviation, while Simpson's method without correction was biased to a negative error and Simpson's method with quadratic correction had a relatively high standard deviation. For sine-squared functions, the errors were generally reduced, with the quadratic correction providing the best result. Based on these evaluations, linear correction is used when integrating sine functions and quadratic correction is used when integrating sine-squared functions.

E. Synchronous Modulation Technique

FIG. 14 illustrates an alternative procedure 1400 for processing the sensor signals. Procedure 1400 is based on synchronous modulation, such as is described by Denys et al., in “Measurement of Voltage Phase for the French Future Defence Plan Against Losses of Synchronism”, IEEE Transactions on Power Delivery, 7(1), 62-69, 1992 and by Begovic et al. in “Frequency Tracking in Power Networks in the Presence of Harmonics”, IEEE Transactions on Power Delivery, 8(2), 480-486, 1993, both of which are incorporated by reference.

First, the controller generates an initial estimate of the nominal operating frequency of the system (step 1405). The controller then attempts to measure the deviation of the frequency of a signal x[k] (e.g., SV₁) from this nominal frequency:

x[k]=A sin [(α₀₊−α)kh+>]+M(k),

where A is the amplitude of the sine wave portion of the signal, α₀ is the nominal frequency (e.g., 88 Hz), −α is the deviation from the nominal frequency, h is the sampling interval, > is the phase shift, and M(k) corresponds to the added noise and harmonics.

To generate this measurement, the controller synthesizes two signals that oscillate at the nominal frequency (step 1410). The signals are phase shifted by 0 and Ξ/2 and have amplitude of unity. The controller multiplies each of these signals by the original signal to produce signals y₁ and y₂ (step 1415):

$\begin{matrix} {y_{1} = {{x\lbrack k\rbrack}{\cos \left( {\omega_{0}{kh}} \right)}}} \\ {{= {{\frac{A}{2}{\sin \left\lbrack {{\left( {{2\omega_{0}} + {\Delta\omega}} \right){kh}} + \Phi} \right\rbrack}} + {\frac{A}{2}{\sin \left( {{{\Delta\omega}\; {kh}} + \Phi} \right)}}}},} \end{matrix}$

and

${y_{2} = {{{x\lbrack k\rbrack}{\sin \left( {\omega_{0}{kh}} \right)}} = {{\frac{A}{2}{\cos \left\lbrack {{\left( {{2\omega_{0}} + {\Delta\omega}} \right){kh}} + \Phi} \right\rbrack}} + {\frac{A}{2}{\cos \left( {{{\Delta\omega}\; {kh}} + \Phi} \right)}}}}},$

where the first terms of y₁ and y₂ are high frequency (e.g., 176 Hz) components and the second terms are low frequency (e.g., 0 Hz) components. The controller then eliminates the high frequency components using a low pass filter (step 1420):

${y_{1}^{i} = {{\frac{A}{2}{\sin \left( {{{\Delta\omega}\; {kh}} + \Phi} \right)}} + {ɛ_{1}\lbrack k\rbrack}}},$

and

${y_{1}^{\prime} = {{\frac{A}{2}{\cos \left( {{{\Delta\omega}\; {kh}} + \Phi} \right)}} + {ɛ_{2}\lbrack k\rbrack}}},$

where ε₁[k] and ε₂[k] represent the filtered noise from the original signals. The controller combines these signals to produce u[k] (step 1425):

$\begin{matrix} {{u\lbrack k\rbrack} = {\left( {{y_{1}^{i}\lbrack k\rbrack} + {j\; {y_{2}^{i}\lbrack k\rbrack}}} \right)\left( {{y_{1}^{i}\left\lbrack {k - 1} \right\rbrack} + {j\; {y_{2}^{i}\left\lbrack {k - 1} \right\rbrack}}} \right)}} \\ {= {{u_{1}\lbrack k\rbrack} + {j\; {u_{2}\lbrack k\rbrack}}}} \\ {{= {{\frac{A^{2}}{4}{\cos \left( {{\Delta\omega}\; h} \right)}} + {j\frac{A^{2}}{4}{\sin \left( {{\Delta\omega}\; h} \right)}}}},} \end{matrix}$

which carries the essential information about the frequency deviation. As shown, u₁[k] represents the real component of u[k], while u₂[k] represents the imaginary component.

The controller uses the real and imaginary components of u[k] to calculate the frequency deviation, −f (step 1430):

${\Delta \; f} = {\frac{1}{h}\arctan {\frac{u_{2}\lbrack k\rbrack}{u_{1}\lbrack k\rbrack}.}}$

The controller then adds the frequency deviation to the nominal frequency (step 1435) to give the actual frequency:

f=Δf+f ₀.

The controller also uses the real and imaginary components of u[k] to determine the amplitude of the original signal. In particular, the controller determines the amplitude as (step 1440):

A ²=4√{square root over (u ₁ ² [k]+u ₂ ² [k])}.

Next, the controller determines the phase difference between the two sensor signals (step 1445). Assuming that any noise (ε₁[k] and ε₂[k]) remaining after application of the low pass filter described below will be negligible, noise free versions of y₁′[k] and y₂′[k] (y₁*[k] and y₂*[k]) may be expressed as:

${{y_{1}^{*}\lbrack k\rbrack} = {\frac{A}{2}{\sin \left( {{{\Delta\omega}\; {kh}} + \Phi} \right)}}},$

and

${y_{2}^{*}\lbrack k\rbrack} = {\frac{A}{2}{{\cos \left( {{{\Delta\omega}\; {kh}} - \Phi} \right)}.}}$

Multiplying these signals together gives:

${v + {y_{1}^{*}y_{2}^{*}}} = {{\frac{A^{2}}{8}\left\lbrack {{\sin \left( {2\; \Phi} \right)} + {\sin \left( {2{\Delta\omega}\; {kh}} \right)}} \right\rbrack}.}$

Filtering this signal by a low pass filter having a cutoff frequency near 0 Hz removes the unwanted component and leaves:

${v^{\prime} - {\frac{A^{2}}{8}{\sin \left( {2\; \Phi} \right)}}},$

from which the phase difference can be calculated as:

$\Phi = {\frac{1}{2}\arcsin {\frac{8v^{\prime}}{A^{2}}.}}$

This procedure relies on the accuracy with which the operating frequency is initially estimated, as the procedure measures only the deviation from this frequency. If a good estimate is given, a very narrow filter can be used, which makes the procedure very accurate. For typical flowmeters, the operating frequencies are around 95 Hz (empty) and 82 Hz (full). A first approximation of half range (88 Hz) is used, which allows a low-pass filter cut-off of 13 Hz. Care must be taken in selecting the cut-off frequency as a very small cut-off frequency can attenuate the amplitude of the sine wave.

The accuracy of measurement also depends on the filtering characteristics employed. The attenuation of the filter in the dead-band determines the amount of harmonics rejection, while a smaller cutoff frequency improves the noise rejection.

F. Meter with PI Control

FIGS. 15A and 15B illustrate a meter 1500 having a controller 1505 that uses another technique to generate the signals supplied to the drivers. Analog-to-digital converters 1510 digitize signals from the sensors 48 and provide the digitized signals to the controller 1505. The controller 1505 uses the digitized signals to calculate gains for each driver, with the gains being suitable for generating desired oscillations in the conduit. The gains may be either positive or negative. The controller 1505 then supplies the gains to multiplying digital-to-analog converters 1515. In other implementations, two or more multiplying digital-to-analog converters arranged in series may be used to implement a single, more sensitive multiplying digital-to-analog converter.

The controller 1505 also generates drive signals using the digitized sensor signals. The controller 1505 provides these drive signals to digital-to-analog converters 1520 that convert the signals to analog signals that are supplied to the multiplying digital-to-analog converters 1515.

The multiplying digital-to-analog converters 1515 multiply the analog signals by the gains from the controller 1505 to produce signals for driving the conduit. Amplifiers 1525 then amplify these signals and supply them to the drivers 46. Similar results could be obtained by having the controller 1505 perform the multiplication performed by the multiplying digital-to-analog converter, at which point the multiplying digital-to-analog converter could be replaced by a standard digital-to-analog converter.

FIG. 15B illustrates the control approach in more detail. Within the controller 1505, the digitized sensor signals are provided to an amplitude detector 1550, which determines a measure, a(t), of the amplitude of motion of the conduit using, for example, the technique described above. A summer 1555 then uses the amplitude a(t) and a desired amplitude a₀ to calculate an error e(t) as:

e(t)=a ₀ −a(t).

The error e(t) is used by a proportional-integral (“PI”) control block 1560 to generate a gain K₀(t). This gain is multiplied by the difference of the sensor signals to generate the drive signal. The PI control block permits high speed response to changing conditions. The amplitude detector 1550, summer 1555, and PI control block 1560 may be implemented as software processed by the controller 1505, or as separate circuitry.

1. Control Procedure

The meter 1500 operates according to the procedure 1600 illustrated in FIG. 16. Initially, the controller receives digitized data from the sensors (step 1605). Thereafter, the procedure 1600 includes three parallel branches: a measurement branch 1610, a drive signal generation branch 1615, and a gain generation branch 1620.

In the measurement branch 1610, the digitized sensor data is used to generate measurements of amplitude, frequency, and phase, as described above (step 1625). These measurements then are used to calculate the mass flow rate (step 1630) and other process variables. In general, the controller 1505 implements the measurement branch 1610.

In the drive signal generation branch 1615, the digitized signals from the two sensors are differenced to generate the signal (step 1635) that is multiplied by the gain to produce the drive signal. As described above, this differencing operation is performed by the controller 1505. In general, the differencing operation produces a weighted difference that accounts for amplitude differences between the sensor signals.

In the gain generation branch 1620, the gain is calculated using the proportional-integral control block. As noted above, the amplitude, a(t), of motion of the conduit is determined (step 1640) and subtracted from the desired amplitude a₀ (step 1645) to calculate the error e(t). Though illustrated as a separate step, generation of the amplitude, a(t), may correspond to generation of the amplitude in the measurement generation step 1625. Finally, the PI control block uses the error e(t) to calculate the gain (step 1650).

The calculated gain is multiplied by the difference signal to generate the drive signal supplied to the drivers (step 1655). As described above, this multiplication operation is performed by the multiplying, D/A converter or may be performed by the controller.

2. PI Control Block

The objective of the PI control block is to sustain in the conduit pure sinusoidal oscillations having an amplitude a₀. The behavior of the conduit may be modeled as a simple mass-spring system that may be expressed as:

{umlaut over (x)}+2ζω_(n) {dot over (x)}+ω _(n) ² x=0,

where x is a function of time and the displacement of the mass from equilibrium, α_(n) is the natural frequency, and N is a damping factor, which is assumed to be small (e.g., 0.001). The solution to this force equation as a function of an output y(t) and an input i(t) is analogous to an electrical network in which the transfer function between a supplied current, i(s), and a sensed output voltage, y(s), is:

$\frac{y(s)}{i(s)} = {\frac{ks}{s^{2} + {2{\zeta\omega}_{n}s} + \omega_{n}^{2}}.}$

To achieve the desired oscillation in the conduit, a positive-feedback loop having the gain K₀(t) is automatically adjusted by a ‘slow’ outer loop to give:

{umlaut over (x)}+(2ζω_(n) −kK ₀(t)){dot over (x)}+ω _(n) ² x=0.

The system is assumed to have a “two-time-scales” property, which means that variations in Kg(t) are slow enough that solutions to the equation for x provided above can be obtained by assuming constant damping.

A two-term PI control block that gives zero steady-state error may be expressed as:

K ₀(t)=K _(p) e(t)+K _(i)∫₀ ^(t) e(t)dt,

where the error, 410 (i.e., a₀-a(t)), is the input to the PI control block, and K_(p) and K_(i) are constants. In one implementation, with a₀=10, controller constants of K_(p)=0.02 and K_(i)=0.0005 provide a response in which oscillations build up quickly. However, this PI control block is nonlinear, which may result in design and operational difficulties.

A linear model of the behavior of the oscillation amplitude may be derived by assuming that x(t) equals AM^(jαt), which results in:

{dot over (x)}={dot over (A)}e ^(jωt) +jωe ^(jωt), and

{umlaut over (x)}=[Ä−ω ² A]e ^(jωt)+2jω{dot over (A)}e ^(jωt).

Substituting these expressions into the expression for oscillation of the loop, and separating into real and imaginary terms, gives:

jω{2{dot over (A)}+(2ζω_(n) −kK ₀)A}=0, and

Ä+(2ζω_(n) −kK ₀){dot over (A)}+(ω_(n) ²−ω²)A=0.

A(t) also may be expressed as:

$\frac{\overset{.}{A}}{A} = {{- {\zeta\omega}_{n}} + {\frac{{kK}_{0}}{2}{t.}}}$

A solution of this equation is:

${\log \; {A(t)}} = {\left( {{- {\zeta\omega}_{n}} + \frac{{kK}_{0}}{2}} \right){t.}}$

Transforming variables by defining a(t) as being equal to log A(t), the equation for A(t) can be written as:

${\frac{a}{t} = {{- {\zeta\omega}_{n}} + \frac{{kK}_{0}(t)}{2}}},$

where K_(o) is now explicitly dependent on time. Taking Laplace transforms results in:

${{a(s)} = \frac{{- {\zeta\omega}_{n}} - {{{kK}_{0}(s)}/2}}{s}},$

which can be interpreted in terms of transfer-functions as in FIG. 17. This figure is of particular significance for the design of controllers, as it is linear for all K_(o) and a, with the only assumption being the two-time-scales property. The performance of the closed-loop is robust with respect to this assumption so that fast responses which are attainable in practice can be readily designed.

From FIG. 17, the term Nα_(n), is a “load disturbance” that needs to be eliminated by the controller (i.e., kK_(o)/2 must equal Nα_(n) for a(t) to be constant). For zero steady-state error this implies that the outer-loop controller must have an integrator (or very large gain). As such, an appropriate PI controller, C(s), may be assumed to be K_(p)(1+1/sT_(i)), where T_(i) is a constant. The proportional term is required for stability. The term Nα_(n), however, does not affect stability or controller design, which is based instead on the open-loop transfer function:

${{C(s)}{G(s)}} = {\frac{a(s)}{e(s)} = {\frac{{kK}_{p}\left( {1 + {sT}_{i}} \right)}{2s^{2}T_{i}} = {\frac{{{kK}_{p}/2}\left( {s + {1/T_{i}}} \right)}{s^{2}}.}}}$

The root locus for varying K is shown in FIG. 18. For small K_(p), there are slow underdamped roots. As K_(p) increases, the roots become real at the point P for which the controller gain is K_(p)=8/(kT_(i)). Note in particular that the theory does not place any restriction upon the choice of T_(i). Hence, the response can, in principle, be made critically damped and as fast as desired by appropriate choices of K_(p) and T_(i).

Although the poles are purely real at point P, this does not mean there is no overshoot in the closed-loop step response. This is most easily seen by inspecting the transfer function between the desired value, a₀, and the error e:

${\frac{e(s)}{a_{0}(s)} = {\frac{s^{2}}{s^{2} + {0.5{{kK}_{p}\left( {s + {1/T_{1}}} \right)}}} = \frac{s^{2}}{p_{2}(s)}}},$

where p₂ is a second-order polynomial. With a step input, a_(o)(s)=I/s, the response can be written as Ip′(t), where p(t) is the inverse transform of 1/p₂(s) and equals a₁exp(−Σ₁t)+a₂exp(−Σ₂t). The signal p(t) increases and then decays to zero so that e(t), which is proportional to p′, must change sign, implying overshoot in a(t). The set-point a_(o) may be prefiltered to give a pseudo set-point a_(o)*:

${{a_{0}^{*}(s)} = {\frac{1}{1 + {sT}_{i}}{a_{0}(s)}}},$

where T_(i) is the known controller parameter. With this prefilter, real controller poles should provide overshoot-free step responses. This feature is useful as there may be physical constraints on overshoot (e.g., mechanical interference or overstressing of components).

The root locus of FIG. 18 assumes that the only dynamics are from the inner-loop's gain/log-amplitude transfer function (FIG. 16) and the outer-loop's PI controller C(s) (i.e., that the log-amplitude a=log A is measured instantaneously). However, A is the amplitude of an oscillation which might be growing or decaying and hence cannot in general be measured without taking into account the underlying sinusoid. There are several possible methods for measuring A, in addition to those discussed above. Some are more suitable for use in quasi-steady conditions. For example, a phase-locked loop in which a sinusoidal signal s(t)=sin(α_(n)t+>₀) locks onto the measured waveform y(t)=A(t)sin(α_(n)t+>₁) may be employed. Thus, a measure of the amplitude a=log A is given by dividing these signals (with appropriate safeguards and filters). This method is perhaps satisfactory near the steady-state but not for start-up conditions before there is a lock.

Another approach uses a peak-follower that includes a zero-crossing detector together with a peak-following algorithm implemented in the controller. Zero-crossing methods, however, can be susceptible to noise. In addition, results from a peal-follower are available only every half-cycle and thereby dictate the sample interval for controller updates.

Finally, an AM detector may be employed. Given a sine wave y(t)=A sin α_(n)t, an estimate of A may be obtained from Â₁0.5ΞF{abs(y)}, where F{ } is a suitable low-pass filter with unity DC gain. The AM detector is the simplest approach. Moreover, it does not presume that there are oscillations of any particular frequency, and hence is usable during startup conditions. It suffers from a disadvantage that there is a leakage of harmonics into the inner loop which will affect the spectrum of the resultant oscillations. In addition, the filter adds extra dynamics into the outer loop such that compromises need to be made between speed of response and spectral purity. In particular, an effect of the filter is to constrain the choice of the best T_(i).

The Fourier series for abs(y) is known to be:

${A\; {{abs}\left( {\sin \; \omega_{n}t} \right)}} = {{\frac{2A}{\pi}\left\lbrack {1 + {\frac{2}{3}\cos \; 2\omega_{n}t} - {\frac{2}{15}\cos \; 4\omega_{n}t} + {\frac{2}{35}\cos \; 6\omega_{n}t} + \ldots} \right\rbrack}.}$

As such, the output has to be scaled by π/2 to give the correct DC output A, and the (even) harmonic terms a_(k) cos 2kω_(n)t have to be filtered out. As all the filter needs to do is to pass the DC component through and reduce all other frequencies, a “brick-wall” filter with a cut-off below 2ω_(n) is sufficient. However, the dynamics of the filter will affect the behavior of the closed-loop. A common choice of filter is in the Butterworth form. For example, the third-order low-pass filter with a design break-point frequency ω_(b) is:

${F(s)} = {\frac{1}{1 + {2{s/\omega_{b}}} + {2{s^{2}/\omega_{b}^{2}}} + {s^{3}/\omega_{b}^{3}}}.}$

At the design frequency the response is 3 dB down; at 2ω_(b) it is −18 dB (0.12), and at 4ω_(b) it is −36 dB (0.015) down. Higher-order Butterworth filters have a steeper roll-off, but most of their poles are complex and may affect negatively the control-loop's root locus.

G. Zero Offset Compensation

As noted above, zero offset may be introduced into a sensor voltage signal by drift in the pre-amplification circuitry and by the analog-to-digital converter. Slight differences in the pre-amplification gains for positive and negative voltages due to the use of differential circuitry may worsen the zero offset effect. The errors vary between transmitters, and with transmitter temperature and component wear.

Audio quality (i.e., relatively low cost) analog-to-digital converters may be employed for economic reasons. These devices are not designed with DC offset and amplitude stability as high priorities. FIGS. 19A-19D show how offset and positive and negative gains vary with chip operating temperature for one such converter (the AD1879 converter). The repeatability of the illustrated trends is poor, and even allowing for temperature compensation based on the trends, residual zero offset and positive/negative gain mismatch remain.

if phase is calculated using the time difference between zero crossing points on the two sensor voltages, DC offset may lead to phase errors. This effect is illustrated by FIGS. 20A-20C. Each graph shows the calculated phase offset as measured by the digital transmitter when the true phase offset is zero (i.e., at zero flow).

FIG. 20A shows phase calculated based on whole cycles starting with positive zero-crossings. The mean value is 0.00627 degrees.

FIG. 20B shows phase calculated starting with negative zero-crossings. The mean value is 0.0109 degrees.

FIG. 20C shows phase calculated every half-cycle. FIG. 20C interleaves the data from FIGS. 20A and 20B. The average phase (−0.00234) is closer to zero than in FIGS. 20A and 20B, but the standard deviation of the signal is about six times higher.

More sophisticated phase measurement techniques, such as those based on Fourier methods, are immune to DC offset. However, it is desirable to eliminate zero offset even when those techniques are used, since data is processed in whole-cycle packets delineated by zero crossing points. This allows simpler analysis of the effects of, for example, amplitude modulation on apparent phase and frequency. In addition, gain mismatch between positive and negative voltages will introduce errors into any measurement technique.

The zero-crossing technique of phase detection may be used to demonstrate the impact of zero offset and gain mismatch error, and their consequent removal. FIGS. 21A and 21B illustrate the long term drift in phase with zero flow. Each point represents an average over one minute of live data. FIG. 21A shows the average phase, and FIG. 21B shows the standard deviation in phase. Over several hours, the drift, is significant. Thus, even if the meter were zeroed every day, which in many applications would be considered an excessive maintenance requirement, there would still be considerable phase drift.

1. Compensation Technique

A technique for dealing with voltage offset and gain mismatch uses the computational capabilities of the digital transmitter and does not require a zero flow condition. The technique uses a set of calculations each cycle which, when averaged over a reasonable period (e.g., 10,000 cycles), and excluding regions of major change (e.g., set point change, onset of aeration), converge on the desired zero offset and gain mismatch compensations.

Assuming the presence of up to three higher harmonics, the desired waveform for a sensor voltage SV(t) is of the form:

SV(t)=A ₁ sin(ωt)+A ₂ sin(2ωt)+A ₃ sin(3ωt)+A ₄ sin(4ωt)

where A₁ designates the amplitude of the fundamental frequency component and A₂-A₄ designate the amplitudes of the three harmonic components. However, in practice, the actual waveform is adulterated with zero offset Z_(o) (which has a value close to zero) and mismatch between the negative and positive gains G_(n) and G_(p). Without any loss of generality, it can be assumed that G_(p) equals one and that G_(n) is given by:

G _(n)=1+M _(G),

where M_(G) represents the gain mismatch.

The technique assumes that the amplitudes A_(i) and the frequency ω are constant. This is justified because estimates of Z_(o) and M_(G) are based on averages taken over many cycles (e.g., 10,000 interleaved cycles occurring in about 1 minute of operation). When implementing the technique, the controller tests for the presence of significant changes in frequency and amplitude to ensure the validity of the analysis. The presence of the higher harmonics leads to the use of Fourier techniques for extracting phase and amplitude information for specific harmonics. This entails integrating SV(t) and multiplying by a modulating sine or cosine function.

The zero offset impacts the integral limits, as well as the functional form. Because there is a zero offset, the starting point for calculation of amplitude and phase will not be at the zero phase point of the periodic waveform SV(t). For zero offset Z_(o), the corresponding phase offset is, approximately,

$\phi_{Z_{0}} = {- {\sin \left( \frac{Z_{0}}{A_{1}} \right)}}$

For small phase,

${\phi_{Z_{o}} = {- \frac{Z_{o}}{A_{1}}}},$

with corresponding time delay

$t_{Z_{o}} = {\frac{\phi_{Z_{o}}}{\omega}.}$

The integrals are scaled so that the limiting value (i.e., as Z and ω_(c), approach zero) equals the amplitude of the relevant harmonic. The first two integrals of interest are:

${I_{1{Ps}} = {\frac{2\omega}{\pi}{\int_{t_{Z_{0}}}^{\frac{\pi}{\omega} + t_{Z_{0}}}{{\left( {{{SV}(t)} + Z_{0}} \right) \cdot {\sin \left\lbrack {\omega \left( {t - t_{Z_{0}}} \right)} \right\rbrack}}\ {t}}}}},$

and

$I_{1\; {Ns}} = {\frac{2\omega}{\pi}\left( {1 + ɛ_{G}} \right){\int_{\frac{\pi}{\omega}t_{z_{0}}}^{{2\frac{\pi}{\omega}} + t_{z_{0}}}{{\left( {{{SV}(t)} + Z_{0}} \right) \cdot {\sin \left\lbrack {\omega \left( {t - t_{z_{0}}} \right)} \right\rbrack}}\ {{t}.}}}}$

These integrals represent what in practice is calculated during a normal Fourier analysis of the sensor voltage data. The subscript 1 indicates the first harmonic, N and P indicate, respectively, the negative or positive half cycle, and s and c indicate, respectively, whether a sine or a cosine modulating function has been used.

Strictly speaking, the mid-zero crossing point, and hence the corresponding integral limits, should be given by π/ω−t_(Zo), rather than π/ω+t_(Zo). However, the use of the exact mid-point rather than the exact zero crossing point leads to an easier analysis, and better numerical behavior (due principally to errors in the location of the zero crossing point). The only error introduced by using the exact mid-point is that a small section of each of the above integrals is multiplied by the wrong gain (1 instead of 1+ε_(G) and vice versa). However, these errors are of order Z_(o) ²ε_(G) and are considered negligible.

Using computer algebra and assuming small Z_(o) and ε_(G), first order estimates for the integrals may be derived as:

${I_{1{{Ps}\_ {est}}} = {A_{1} + {\frac{4}{\pi}{Z_{0}\left\lbrack {1 + {\frac{2}{3}\frac{A_{2}}{A_{1}}} + {\frac{4}{15}\frac{A_{4}}{A_{1}}}} \right\rbrack}}}},$

and

$I_{1\; {Ns\_ est}} = {{\left( {1 + ɛ_{G}} \right)\left\lbrack {A_{1} - {\frac{4}{\pi}{Z_{0}\left\lbrack {1 + {\frac{2}{3}\frac{A_{2}}{A_{1}}} + {\frac{4}{15}\frac{A_{4}}{A_{1}}}} \right\rbrack}}} \right\rbrack}.}$

Useful related functions including the sum, difference, and ratio of the integrals and their estimates may be determined. The sum of the integrals may be expressed as:

SUM_(1s)=(I _(1Ps) +I _(1Ns)),

while the sum of the estimates equals:

${Sum}_{1{s\_ {est}}} = {{A_{1}\left( {2 + ɛ_{G}} \right)} - {\frac{4}{\pi}Z_{0}{{ɛ_{G}\left\lbrack {1 + {\frac{2}{3}\frac{A_{2}}{A_{1}}} + {\frac{4}{15}\frac{A_{4}}{A_{1}}}} \right\rbrack}.}}}$

Similarly, the difference of the integrals may be expressed as:

Diff_(1s) =I _(1Ps) −I _(1Ns),

while the difference of the estimates is:

${Diff}_{1{s\_ {est}}} = {{A_{1}ɛ_{G}} + {\frac{4}{\pi}{{{Z_{0}\left( {2 + ɛ_{G}} \right)}\left\lbrack {1 + {\frac{2}{3}\frac{A_{2}}{A_{1}}} + {\frac{4}{15}\frac{A_{4}}{A_{1}}}} \right\rbrack}.}}}$

Finally, the ratio of the integrals is:

${{Ratio}_{1s} = \frac{I_{1{Ps}}}{I_{1{Ns}}}},$

while the ratio of the estimates is:

${Ratio}_{1{s\_ est}} = {{\frac{1}{1 + ɛ_{G}}\left\lbrack {1 + {Z_{0}\left\lbrack {\frac{8}{15}\frac{{15A_{1}} + {10A_{2}} + {4A_{4}}}{{\pi A}_{1}^{2}}} \right\rbrack}} \right\rbrack}.}$

Corresponding cosine integrals are defined as:

${I_{1{Pc}} = {\frac{2\omega}{\pi}{\int_{t_{Z_{0}}}^{\frac{\pi}{\omega} + t_{Z_{0}}}{\left( {{{SV}(t)} + Z_{0}} \right){\cos \left\lbrack {\omega \left( {t - t_{Z_{0}}} \right)} \right\rbrack}\ {t}}}}},$

and

${I_{1\; {Nc}} = {\frac{2\omega}{\pi}\left( {1 + ɛ_{G}} \right){\int_{\frac{\pi}{\omega} + t_{z_{0}}}^{\frac{2\pi}{\omega} + t_{z_{0}}}{\left( {{{SV}(t)} + Z_{0}} \right){\cos \left\lbrack {\omega \left( {t - t_{z_{0}}} \right)} \right\rbrack}\ {t}}}}},$

with estimates:

${I_{1{{Pc}\_ {est}}} = {{- Z_{0}} + \frac{{40A_{2}} + {16A_{4}}}{15\pi}}},$

and

${I_{1\; {Nc\_ est}} = {\left( {1 + ɛ_{G}} \right)\left\lbrack {Z_{0} + \frac{{40\; A_{2}} + {16\; A_{4}}}{15\pi}} \right\rbrack}},$

and sums:

Sum_(1C) =I _(1Pc) +I _(1Nc), and

${Sum}_{1{C\_ {est}}} = {{ɛ_{G}\left\lbrack {Z_{0} + \frac{{40A_{2}} + {16A_{4}}}{15\pi}} \right\rbrack}.}$

Second harmonic integrals are:

${I_{2{Ps}} = {\frac{2\omega}{\pi}{\int_{t_{Z_{0}}}^{\frac{\pi}{\omega} + t_{Z_{0}}}{\left( {{{SV}(t)} + Z_{0}} \right){\sin \left\lbrack {2{\omega \left( {t - t_{Z_{0}}} \right)}} \right\rbrack}\ {t}}}}},$

and

$I_{2\; {Ns}} = {\frac{2\omega}{\pi}\left( {1 + ɛ_{G}} \right){\int_{\frac{\pi}{\omega} + t_{z_{0}}}^{\frac{2\pi}{\omega} + t_{z_{0}}}\left( {{{{SV}(t)} + {Z_{o}{\sin \left\lbrack {2{\omega \left( {t - t_{z_{0}}} \right)}} \right\rbrack}\ {t}}},} \right.}}$

with estimates:

${I_{2{{Ps}\_ {est}}} = {A_{2} + {\frac{8}{15\pi}{Z_{0}\left\lbrack {{- 5} + {9\frac{A_{3}}{A_{1}}}} \right\rbrack}}}},$

and

${I_{2\; {Ps\_ est}} = {\left( {1 + ɛ_{G}} \right)\left\lbrack {A_{2} - {\frac{8}{15\pi}{Z_{0}\left\lbrack {{- 5} + {9\frac{A_{3}}{A_{1}}}} \right\rbrack}}} \right\rbrack}},$

and sums:

Sum_(2s) =I _(2Ps) +I _(2Ns), and

${Sum}_{2{{Ps}\_ {est}}} = {{A_{2}\left( {2 + ɛ_{G}} \right)} - {\frac{8}{15\pi}ɛ_{G}{{Z_{0}\left\lbrack {{- 5} + {9\frac{A_{3}}{A_{1}}}} \right\rbrack}.}}}$

The integrals can be calculated numerically every cycle. As discussed below, the equations estimating the values of the integrals in terms of various amplitudes and the zero offset and gain values are rearranged to give estimates of the zero offset and gain terms based on the calculated integrals.

2. Example

The accuracy of the estimation equations may be illustrated with an example. For each basic integral, three values are provided: the “true” value of the integral (calculated within Mathcad using Romberg integration), the value using the estimation equation, and the value calculated by the digital transmitter operating in simulation mode, using Simpson's method with end correction.

Thus, for example, the value for (I_(1Ps) _(—) _(est))calculated according to:

$I_{1{Ps}} = {\frac{2\omega}{\pi}{\int_{t_{Z_{0}}}^{\frac{\pi}{\omega} + t_{Z_{0}}}{\left( {{{SV}(t)} + Z_{0}} \right){\sin \left\lbrack {\omega \left( {t - t_{Z_{0}}} \right)} \right\rbrack}\ {t}}}}$

is 0.101353, while the estimated value (I calculated as:

$I_{1{{Ps}\_ {est}}} = {A_{1} + {\frac{4}{\pi}{Z_{0}\left\lbrack {1 + {\frac{2}{3}\frac{A_{2}}{A_{1}}} + {\frac{4}{15}\frac{A_{4}}{A_{1}}}} \right\rbrack}}}$

is 0.101358. The value calculated using the digital transmitter in simulation mode is 0.101340. These calculations use the parameter values illustrated in Table C.

TABLE C Parameter Value Comment α 160π This corresponds to frequency = 80 Hz, a typical value. A₁ 0.1 This more typically is 0.3, but it could be smaller with aeration. A₂ 0.01 This more typically is 0.005, but it could be larger with aeration. A₃ and A₄ 0.0 The digital Coriolis simulation mode only offers two harmonics, so these higher harmonics are ignored. However, they are small (<0.002). Z₀ 0.001 Experience suggests that this is a large value for zero offset. M_(G) 0.001 Experience suggests this is a large value for gain mis- match. The exact, estimate and simulation results from using these parameter values are illustrated in Table D.

TABLE D Integral ‘Exact’ Value Estimate Digital Coriolis Simulation I_(1Ps) 0.101353 0.101358 0.101340 I_(1Ns) 0.098735 0.098740 0.098751 I_(1Pc) 0.007487 0.007488 0.007500 I_(1Nc) −0.009496 −0.009498 −0.009531 I_(2Ps) 0.009149 0.009151 0.009118 I_(2Ns) 0.010857 0.010859 0.010885

Thus, at least for the particular values selected, the estimates given by the first order equations are extremely accurate. As Z_(o) and ε_(G) approach zero, the errors in both the estimate and the simulation approach zero.

3. Implementation

The first order estimates for the integrals define a series of non-linear equations in terms of the amplitudes of the harmonics, the zero offset, and the gain mismatch. As the equations are nonlinear, an exact solution is not readily available. However, an approximation followed by corrective iterations provides reasonable convergence with limited computational overhead.

Conduit-specific ratios may be assumed for A₁-A₄. As such, no attempt is made to calculate all of the amplitudes A₁-A₄. Instead, only A₁ and A₂ are estimated using the integral equations defined above. Based on experience of the relative amplitudes, A₃ may be approximated as A₂/2, and A₄ may be approximated as A₂/10. The zero offset compensation technique may be implemented according to the procedure 2200 illustrated in FIG. 22. During each cycle, the controller calculates the integrals I_(1Ps), I_(1Ns), I_(1Pc), I_(1Nc), I_(2Ps), I_(2Ns) and related functions sum_(1s), ratio_(1s), sum_(1c) and sum_(2s), (step 2205). This requires minimal additional calculation beyond the conventional Fourier calculations used to determine frequency, amplitude and phase.

Every 10,000 cycles, the controller checks on the slope of the sensor voltage amplitude A₁, using a conventional rate-of-change estimation technique (step 2210). If the amplitude is constant (step 2215), then the controller proceeds with calculations for zero offset and gain mismatch. This cheek may be extended to test for frequency stability.

To perform the calculations, the controller generates average values for the functions (e.g., sum_(1s)) over the last 10,000 cycles. The controller then makes a first estimation of zero offset and gain mismatch (step 2225):

Z ₀=−Sum_(1c)/2, and

M _(G)=1/Ratio_(1s)−1

Using these values, the controller calculates an inverse gain factor (k) and amplitude factor (amp_factor) (step 2230):

k=1.0/(1.0+0.5*M _(G)), and

amp_factor=1+50/75*Sum_(2s)/Sum_(1s)

The controller uses the inverse gain factor and amplitude factor to make a first estimation of the amplitudes (step 2235):

A ₁ =k*[Sum_(1s)/2+2/π*Z _(o)*ε_(G)*amp_factor], and

A ₂ =k*[Sum_(2s)/2−4/(3*π)*Z _(o)*ε_(G)

The controller then improves the estimate by the following calculations, iterating as required (step 2240):

x ₁ =Z _(o),

x ₂=ε_(G),

ε_(G)=[1+8/π*x ₁ /A ₁*amp_factor]/Ratio_(1s)−1.0,

Z _(o)=−Sum_(1c)/2+x ₂*(x ₁+2.773/π*A ₂)/2,

A ₁ =k*[Sum_(1s)/2+2/π*x ₁ *x ₂*amp_factor],

A ₂ =k*[Sum_(2s)/2−4/(15*π)*x ₁ *x ₂*(5−4.5*A ₂)].

The controller uses standard techniques to test for convergence of the values of Z_(o) and ε_(G). In practice the corrections are small after the first iteration, and experience suggests that three iterations are adequate.

Finally, the controller adjusts the raw data to eliminate Z_(o) and ε_(G) (step 2245). The controller then repeats the procedure. Once zero offset and gain mismatch have been eliminated from the raw data, the functions (i.e., sum_(1s)) used in generating subsequent values for Z_(o) and ε_(G) are based on corrected data. Accordingly, these subsequent values for Z_(o) and ε_(G) reflect residual zero offset and gain mismatch, and are summed with previously generated values to produce the actual zero offset and gain mismatch. In one approach to adjusting the raw data, the controller generates adjustment parameters (e.g., S1_off and S2_off) that are used in converting the analog signals from the sensors to digital data.

FIGS. 23A-23C, 24A and 24B show results obtained using the procedure 2200. The short-term behavior is illustrated in FIGS. 23A-23C. This shows consecutive phase estimates obtained five minutes after startup to allow time for the procedure to begin affecting the output. Phase is shown based on positive zero-crossings, negative zero-crossings, and both.

The difference between the positive and negative mean values has been reduced by a factor of 20, with a corresponding reduction in mean zero offset in the interleaved data set. The corresponding standard deviation has been reduced by a factor of approximately 6.

Longer term behavior is shown in FIGS. 24A and 24B. The initial large zero offset is rapidly corrected, and then the phase offset is kept close to zero over many hours. The average phase offset, excluding the first few values, is 6.14e⁻⁶, which strongly suggests that the procedure is successful in compensating for changes in voltage offset and gain imbalance.

Typical values for Z_(o) and ε_(G) for the digital Coriolis meter are Z_(o)=−7.923e⁻⁴ and ε_(G)=−1.754e⁻⁵ for signal SV₁, and Z_(o)=−8.038e⁻⁴ and ε_(G)+6.93e⁻⁴ for signal SV₂.

In general, conventional measurement calculations for Coriolis meters assume that the frequency and amplitude of oscillation on each side of the conduit are constant, and that the frequency on each side of the conduit is identical and equal to the so-called resonant frequency. Phases generally are not measured separately for each side of the conduit, and the phase difference between the two sides is assumed to be constant for the duration of the measurement process. Precise measurements of frequency, phase and amplitude every half-cycle using the digital meter demonstrate that these assumptions are only valid when parameter values are averaged over a time period on the order of seconds. Viewed at 100 Hz or higher frequencies, these parameters exhibit considerable variation. For example, during normal operation, the frequency and amplitude values of SV₁ may exhibit strong negative correlation with the corresponding SV₂ values. Accordingly, conventional measurement algorithms are subject to noise attributable to these dynamic variations. The noise becomes more significant as the measurement calculation rate increases. Other noise tea ins may be introduced by physical factors, such as flowtube dynamics, dynamic non-linearities (e.g. flowtube stiffness varying with amplitude), or the dynamic consequences of the sensor voltages providing velocity data rather than absolute position data.

The described techniques exploit the high precision of the digital meter to monitor and compensate for dynamic conduit behavior to reduce noise so as to provide more precise measurements of process variables such as mass flow and density. This is achieved by monitoring and compensating for such effects as the rates of change of frequency, phase and amplitude, flowtube dynamics, and dynamic physical non-idealities. A phase difference calculation which does not assume the same frequency on each side has already been described above. Other compensation techniques are described below.

Monitoring and compensation for dynamic effects may take place at the individual sensor level to provide corrected estimates of phase, frequency, amplitude or other parameters. Further compensation may also take place at the conduit level, where data from both sensors are combined, for example in the calculation of phase difference and average frequency. These two levels may be used together to provide comprehensive compensation.

Thus, instantaneous mass flow and density measurements by the flowmeter may be improved by modelling and accounting for dynamic effects of flowmeter operation. In general, 80% or more of phase noise in a Coriolis flowmeter may be attributed to flowtube dynamics (sometimes referred to as “ringing”), rather than to process conditions being measured. The application of a dynamic model can reduce phase noise by a factor of 4 to 10, leading to significantly improved flow measurement performance. A single model is effective for all flow rates and amplitudes of oscillation. Generally, computational requirements are negligible.

The dynamic analysis may be performed on each of the sensor signals in isolation from the other. This avoids, or at least delays, modelling the dynamic interaction between the two sides of the conduit, which is likely to be far more complex than the dynamics at each sensor. Also, analyzing the individual sensor signals is more likely to be successful in circumstances such as batch startup and aeration where the two sides of the conduit are subject to different forces from the process fluid.

In general, the dynamic analysis considers the impact of time-varying amplitude, frequency and phase on the calculated values for these parameters. While the frequency and amplitude are easily defined for the individual sensor voltages, phase is conventionally defined in terms of the difference between the sensor voltages. However, when a Fourier analysis is used, phase for the individual sensor may be defined in terms of the difference between the midpoint of the cycle and the average 180° phase point.

Three types of dynamic effects are measurement error and the so-called “feedback” and “velocity” effects. Measurement error results because the algorithms for calculating amplitude and phase assume that frequency, amplitude, and phase are constant over the time interval of interest. Performance of the measurement algorithms may be improved by correcting for variations in these parameters.

The feedback effect results from supplying energy to the conduit to make up for energy loss from the conduit so as to maintain a constant amplitude of oscillation. The need to add energy to the conduit is only recognized after the amplitude of oscillation begins to deviate from a desired setpoint. As a result, the damping term in the equation of motion for the oscillating conduit is not zero, and, instead, constantly dithers around zero. Although the natural frequency of the conduit does not change, it is obscured by shifts in the zero-crossings (i.e., phase variations) associated with these small changes in amplitude.

The velocity effect results because the sensor voltages observe conduit velocity, but are analyzed as being representative of conduit position. A consequence of this is that the rate of change of amplitude has an impact on the apparent frequency and phase, even if the true values of these parameters are constant.

1. Sensor-Level Compensation for Amplitude Modulation

One approach to correcting for dynamic effects monitors the amplitudes of the sensor signals and makes adjustments based on variations in the amplitudes. For purposes of analyzing dynamic effects, it is assumed that estimates of phase, frequency and amplitude may be determined for each sensor voltage during each cycle. As shown in FIG. 25, calculations are based on complete but overlapping cycles. Each cycle starts at a zero crossing point, halfway through the previous cycle. Positive cycles begin with positive voltages immediately after the initial zero-crossing, while negative cycles begin with negative voltages. Thus cycle it is positive, while cycles n−1 and n+1 are negative. It is assumed that zero offset correction has been performed so that zero offset is negligible. It also is assumed that higher harmonics may be present.

Linear variation in amplitude, frequency, and phase are assumed. Under this assumption, the average value of each parameter during a cycle equals the instantaneous value of the parameter at the mid-point of the cycle. Since the cycles overlap by 180 degrees, the average value for a cycle equals the starting value for the next cycle.

For example, cycle n is from time 0 to 2π/ω. The average values of amplitude, frequency and phase equal the instantaneous values at the mid-point, π/ω, which is also the starting point for cycle n+1, which is from time π/ω to 3π/ω. Of course, these timings are approximate, since ω also varies with time.

a. Dynamic Effect Compensation Procedure

The controller accounts for dynamic effects according to the procedure 2600 illustrated in FIG. 26. First, the controller produces a frequency estimate (step 2605) by using the zero crossings to measure the time between the start and end of the cycle, as described above. Assuming that frequency varies linearly, this estimate equals the time-averaged frequency over the period.

The controller then uses the estimated frequency to generate a first estimate of amplitude and phase using the Fourier method described above (step 2610). As noted above, this method eliminates the effects of higher harmonics.

Phase is interpreted in the context of a single waveform as being the difference between the start of the cycle (i.e., the zero-crossing point) and the point of zero phase for the component of SV(t) of frequency ω, expressed as a phase offset. Since the phase offset is an average over the entire waveform, it may be used as the phase offset from the midpoint of the cycle. Ideally, with no zero offset and constant amplitude of oscillation, the phase offset should be zero every cycle. In practice, however, it shows a high level of variation and provides an excellent basis for correcting mass flow to account for dynamic changes in amplitude.

The controller then calculates a phase difference (step 2615). Though a number definitions of phase difference are possible, the analysis assumes that the average phase and frequency of each sensor signal is representative of the entire waveform. Since these frequencies are different for SV₁ and SV₂, the corresponding phases are scaled to the average frequency. In addition, the phases are shifted to the same starting point (i.e., the midpoint of the cycle on SV₁). After scaling, they are subtracted to provide the phase difference.

The controller next determines the rate of change of the amplitude for the cycle n (step 2620):

$\begin{matrix} {{roc\_ amp}_{n} \approx \frac{{{amp}\mspace{14mu} \left( {{end}\mspace{14mu} {of}\mspace{14mu} {cycle}} \right)} - {{amp}\mspace{14mu} \left( {{start}\mspace{14mu} {of}{\mspace{11mu} \;}{cycle}} \right)}}{{period}\mspace{14mu} {of}\mspace{14mu} {cycle}}} \\ {= {\left( {{amp}_{n + 1} - {amp}_{n - 1}} \right){{freq}_{n}.}}} \end{matrix}$

This calculation assumes that the amplitude from cycle n+1 is available when calculating the rate of change of cycle rt. This is possible if the corrections are made one cycle after the raw amplitude calculations have been made. The advantage of having an accurate estimate of the rate of change, and hence good measurement correction, outweighs the delay in the provision of the corrected measurements, which, in one implementation, is on the order of 5 milliseconds. The most recently generated information is always used for control of the conduit (i.e., for generation of the drive signal).

If desired, a revised estimate of the rate of change can be calculated after amplitude correction has been applied (as described below). This results in iteration to convergence for the best values of amplitude and rate of change.

b. Frequency Compensation for Feedback and Velocity Effects

As noted above, the dynamic aspects of the feedback loop introduce time varying shifts in the phase due to the small deviations in amplitude about the set-point. This results in the measured frequency, which is based on zero-crossings, differing from the natural frequency of the conduit. If velocity sensors are used, an additional shift in phase occurs. This additional shift is also associated with changes in the positional amplitude of the conduit. A dynamic analysis can monitor and compensate for these effects. Accordingly, the controller uses the calculated rate of amplitude change to correct the frequency estimate (step 2625).

The position of an oscillating conduit in a feedback loop that is employed to maintain the amplitude of oscillation of the conduit constant may be expressed as:

X=A(t)sin(ω₀ t−θ(t)),

where θ(t) is the phase delay caused by the feedback effect. The mechanical Q of the oscillating conduit is typically on the order of 1000, which implies small deviations in amplitude and phase. Under these conditions, θ(t) is given by:

${\theta (t)} \approx {- {\frac{\overset{.}{A}(t)}{2\omega_{0}{A(t)}}.}}$

Since each sensor measures velocity:

$\begin{matrix} {{{SV}(t)} = {\overset{.}{X}(t)}} \\ {= {{{\overset{.}{A}(t)}{\sin \left\lbrack {{\omega_{0}t} - {\theta (t)}} \right\rbrack}} + {\left\lbrack {\omega_{0} - {\overset{.}{\theta}(t)}} \right\rbrack {A(t)}{\cos \left\lbrack {{\omega_{0}t} - {\theta (t)}} \right\rbrack}}}} \\ {{= {\omega_{0}{{A(t)}\left\lbrack {\left( {1 - \frac{\theta}{\omega_{0}}} \right)^{2} + \left( \frac{\overset{.}{A}(t)}{\omega_{0}{A(t)}} \right)^{2}} \right\rbrack}^{1/2}{\cos \left( {{\omega_{0}t} - {\theta (t)} - {\gamma (t)}} \right)}}},} \end{matrix}$

where γ(t) is the phase delay caused by the velocity effect:

${\gamma (t)} = {{\tan^{- 1}\left( \frac{\overset{.}{A}(t)}{\omega_{0}{A(t)}\left( {1 - \frac{\overset{.}{\theta}}{\omega_{0}}} \right)} \right)}.}$

Since the mechanical Q of the conduit is typically on the order of 1000, and, hence, variations in amplitude and phase are small, it is reasonable to assume:

$\frac{\overset{.}{\theta}}{\omega_{0}}{\operatorname{<<}1}\mspace{14mu} {and}\mspace{14mu} \frac{\overset{.}{A}(t)}{\omega_{0}{A(t)}}{\operatorname{<<}1.}$

This means that the expression for SV(t) may be simplified to:

SV(t)≈ω₀ A(t)cos(ω₀ t−θ(t)−γ(t)),

and for the same reasons, the expression for the velocity offset phase delay may be simplified to:

${\gamma (t)} \approx {\frac{\overset{.}{A}(t)}{\omega_{0}{A(t)}}.}$

Summing the feedback and velocity effect phase delays gives the total phase delay:

${{\phi (t)} = {{{{\theta (t)} + {\gamma (t)}} \approx {{- \frac{\overset{.}{A}(t)}{2\omega_{0}{A(t)}}} + \frac{\overset{.}{A}(t)}{\omega_{0}{A(t)}}}} = \frac{\overset{.}{A}(t)}{2\omega_{0}{A(t)}}}},$

and the following expression for SV(t):

SV(t)≈ω₀ A(t)cos [(ω₀ t−φ(t)].

From this, the actual frequency of oscillation may be distinguished from the natural frequency of oscillation. Though the former is observed, the latter is useful for density calculations. Over any reasonable length of time, and assuming adequate amplitude control, the averages of these two frequencies are the same (because the average rate of change of amplitude must be zero). However, for improved instantaneous density measurement, it is desirable to compensate the actual frequency of oscillation for dynamic effects to obtain the natural frequency. This is particularly useful in dealing with aerated fluids for which the instantaneous density can vary rapidly with time.

The apparent frequency observed for cycle n is delineated by zero crossings occurring at the midpoints of cycles n−1 and nil. The phase delay due to velocity change will have an impact on the apparent start and end of the cycle:

$\begin{matrix} {{obs\_ freq}_{n} = {{obs\_ freq}_{n - 1} + {\frac{{true\_ freq}_{n}}{2\pi}\left( {\phi_{n + 1} - \phi_{n - 1}} \right)}}} \\ {{{obs\_ freq}_{n - 1} + {\frac{{true\_ freq}_{n - 1}}{2\pi}\begin{pmatrix} {\frac{{\overset{.}{A}}_{n + 1}}{4{\pi true\_ freq}_{n}A_{n + 1}} -} \\ \frac{{\overset{.}{A}}_{n - 1}}{4{\pi true\_ freq}_{n}A_{n - 1}} \end{pmatrix}}}} \\ {{{obs\_ freq}_{n - 1} + {\frac{1}{8\pi^{2}}{\left( {\frac{{\overset{.}{A}}_{n + 1}}{A_{n + 1}} - \frac{{\overset{.}{A}}_{n - 1}}{A_{n - 1}}} \right).}}}} \end{matrix}$

Based on this analysis, a correction can be applied using an integrated error term:

${{error\_ sum}_{n} = {{error\_ sum}_{n - 1} - {\frac{1}{8\pi^{2}}\left( {\frac{{\overset{.}{A}}_{n + 1}}{A_{n + 1}} - \frac{{\overset{.}{A}}_{n - 1}}{A_{n - 1}}} \right)}}},$

and

est_freq_(n)=obs_freq_(n)−error_sum_(n),

where the value of error_sum at startup (i.e., the value at cycle zero) is:

${error\_ sum}_{0} = {{- \frac{1}{8\pi^{2}}}{\left( {\frac{{\overset{.}{A}}_{0}}{A_{0}} + \frac{{\overset{.}{A}}_{1}}{A_{1}}} \right).}}$

Though these equations include a constant term having a value of ⅛π², actual data has indicated that a constant term of ⅛π is more appropriate. This discrepancy may be due to unmodelled dynamics that may be resolved through further analysis.

The calculations discussed above assume that the true amplitude of oscillation, A, is available. However, in practice, only the sensor voltage SV is observed. This sensor voltage may be expressed as:

SV(t))≈ω₀){dot over (A)}(t)cos(ω₀ t−φ(t))

The amplitude, amp_SV(t), of this expression is:

amp_SV(t)≈ω₀ {dot over (A)}(t).

The rate of change of this amplitude is:

roc_amp_SV(t)≈ω₀ {dot over (A)}(t)

so that the following estimation can be used:

$\frac{\overset{.}{A}(t)}{A(t)} \approx {\frac{{roc\_ amp}{\_ SV}(t)}{{amp\_ SV}(t)}.}$

c. Application of Feedback and Velocity Effect Frequency Compensation

FIGS. 27A-32B illustrate how application of the procedure 2600 improves the estimate of the natural frequency, and hence the process density, for real data from a meter having a one inch diameter conduit. Each of the figures shows 10,000 samples, which are collected in just over 1 minute.

FIGS. 27A and 27B show amplitude and frequency data from SV₁, taken when random chances to the amplitude set-point have been applied. Since the conduit is full of water and there is no flow, the natural frequency is constant. However, the observed frequency varies considerably in response to changes in amplitude. The mean frequency value is 81.41 Hz, with a standard deviation of 0.057 Hz.

FIGS. 28A and 28B show, respectively, the variation in frequency from the mean value, and the correction term generated using the procedure 2600. The gross deviations are extremely well matched. However, there is additional variance in frequency which is not attributable to amplitude variation. Another important feature illustrated by FIG. 28B is that the average is close to zero as a result of the proper initialization of the error term, as described above.

FIGS. 29A and 92B compare the raw frequency data (FIG. 29A) with the results of applying the correction function (FIG. 29B). There has been a negligible shift in the mean frequency, while the standard deviation has been reduced by a factor of 4.4. From FIG. 29B, it is apparent that there is residual structure in the corrected frequency data. It is expected that further analysis, based on the change in phase across a cycle and its impact on the observed frequency, will yield further noise reductions.

FIGS. 30A and 30B show the corresponding effect on the average frequency, which is the mean of the instantaneous sensor voltage frequencies. Since the mean frequency is used to calculate the density of the process fluid, the noise reduction (here by a factor of 5.2) will be propagated into the calculation of density.

FIGS. 31A and 31B illustrate the raw and corrected average frequency for a 2″ diameter conduit subject to a random amplitude set-point. The 2″ flowtube exhibits less frequency variation that the 1″, for both raw and corrected data. The noise reduction factor is 4.0.

FIGS. 32A and 32B show more typical results with real flow data for the one inch flowtube. The random setpoint algorithm has been replaced by the normal constant setpoint. As a result, there is less amplitude variation than in the previous examples, which results in a smaller noise reduction factor of 1.5.

d. Corn sensation of Phase Measurement for Amplitude Modulation

Referring again to FIG. 26, the controller next compensates the phase measurement to account for amplitude modulation assuming the phase calculation provided above (step 2630). The Fourier calculations of phase described above assume that the amplitude of oscillation is constant throughout the cycle of data on which the calculations take place. This section describes a correction which assumes a linear variation in amplitude over the cycle of data.

Ignoring higher harmonics, and assuming that any zero offset has been eliminated, the expression for the sensor voltage is given by:

SV(t)≈A ₁(1+λ_(A) t)sin(ωt)

where Σ_(A) is a constant corresponding to the relative change in amplitude with time. As discussed above, the integrals I₁ and I₂ may be expressed as:

${I_{1} = {\frac{2\omega}{\pi}{\int_{0}^{\frac{2\pi}{\omega}}{{{SV}(t)}{\sin \left( {\omega \; t} \right)}\ {t}}}}},$

and

$I_{2} = {\frac{2\omega}{\pi}{\int_{0}^{\frac{2\pi}{\omega}}{{{SV}(T)}{\cos \left( {\omega \; t} \right)}\ {{t}.}}}}$

Evaluating these integrals results in:

${I_{1} = {A_{1}\left( {1 + {\frac{\pi}{\omega}\lambda_{A}}} \right)}},$

and

$I_{2} = {A_{1}\frac{1}{2\omega}{\lambda_{A}.}}$

Substituting these expressions into the calculation for amplitude and expanding as a series in Σ_(A) results in:

${Amp} = {{A_{1}\left( {1 + {\frac{\pi}{\omega}\lambda_{A}} + {\frac{1}{8\omega^{2}}\lambda_{A}^{2}} + \ldots}\mspace{14mu} \right)}.}$

Assuming Σ_(A) is small, and ignoring all terms after the first order term, this may be simplified to:

${Amp} = {{A_{1}\left( {1 + {\frac{\pi}{\omega}\lambda_{A}}} \right)}.}$

This equals the amplitude of SV(t) at the midpoint of the cycle (t=π/ω). Accordingly, the amplitude calculation provides the required result without correction.

For the phase calculation, it is assumed that the true phase difference and frequency are constant, and that there is no voltage offset, which means that the phase value should be zero. However, as a result of amplitude modulation, the correction to be applied to the raw phase data to compensate for amplitude modulation is:

${Phase} = {{\tan^{- 1}\left( \frac{\lambda_{A}}{2\left( {{\pi\lambda}_{A} + \omega} \right)} \right)}.}$

Assuming that the expression in brackets is small, the inverse tangent function can be ignored.

A more elaborate analysis considers the effects of higher harmonics. Assuming that the sensor voltage may be expressed as:

SV(t)=(1+λ_(A) t)[A ₁ sin(ωt)+A ₂ sin(2ωt)+A ₃ sin(ωt)+A ₄ sin(4ωt)]

such that all harmonic amplitudes increase at the same relative rate over the cycle, then the resulting integrals may be expressed as:

${I_{1} = {A_{1}\left( {1 + {\frac{\pi}{\omega}\lambda_{A}}} \right)}},$

and

$I_{2}\frac{- 1}{60\omega}{\lambda_{A}\left( {{30\; A_{1}} + {80\; A_{2}} + {45\; A_{3}} + {32\; A_{4}}} \right)}$

for positive cycles, and

$I_{2}\frac{- 1}{60\omega}{\lambda_{A}\left( {{30A_{1}} - {80A_{2}} + {45A_{3}} - {32A_{4}}} \right)}$

for negative cycles.

For amplitude, substituting these expressions into the calculations establishes that the amplitude calculation is only affected in the second order and higher terms, so that no correction is necessary to a first order approximation of the amplitude. For phase, the correction term becomes:

$\frac{- 1}{60}{\lambda_{A}\left( \frac{{30A_{1}} - {80A_{2}} + {45A_{3}} + {32A_{4}}}{A_{1}\left( {{\pi\lambda}_{A} + \omega} \right)} \right)}$

for positive cycles, and

$\frac{- 1}{60}{\lambda_{A}\left( \frac{{30A_{1}} - {80A_{2}} + {45A_{3}} + {32A_{4}}}{A_{1}\left( {{\pi\lambda}_{A} + \omega} \right)} \right)}$

for negative cycles. These correction terms assume the availability of the amplitudes of the higher harmonies. While these can be calculated using the usual Fourier technique, it is also possible to approximate some or all them using assumed ratios between the harmonics. For example, for one implementation of a one inch diameter conduit, typical amplitude ratios are A₁=1.0, A₂=0.01, A₃=0.005, and A₄=0.001.

e. Application of Amplitude Modulation Compensation to Phase

Simulations have been carried out using the digital transmitter, including the simulation of higher harmonies and amplitude modulation. One example uses f===80 Hz, A₁ (t=0)=0.3, A₂=0, A₃=0, A₄=0, Σ_(A)=1e⁻⁵*48 KHz (sampling rate)=0.47622, which corresponds to a high rate of change of amplitude, but with no higher harmonics. Theory suggests a phase offset of −0.02706 degrees. In simulation over 1000 cycles, the average offset is −0.02714 degrees, with a standard deviation of only 2.17e⁻⁶. The difference between simulation and theory (approx 0.3% of the simulation error) is attributable to the model's assumption of a linear variation in amplitude over each cycle, while the simulation generates an exponential change in amplitude.

A second example includes a second harmonic, and has the parameters f=80 Hz, A₁ (t=0)=0.3, A₂ (t=0)=0.003, A₃=0, A₄=0, Σ_(A)=−1e⁻⁶*48 KHz (sampling rate)=−0.047622. For this example, theory predicts the phase offset to be +2.706e⁻³, +/−2.66% for positive or negative cycles. In simulation, the results are 2.714e⁻³+/−2.66%, which again matches well.

FIGS. 33A-34B give examples of how this correction improves real flowmeter data. FIG. 33A shows raw phase data from SV₁, collected from a 1″ diameter conduit, with low flow assumed to be reasonably constant. FIG. 33B shows the correction factor calculated using the formula described above, while FIG. 33C shows the resulting corrected phase. The most apparent feature is that the correction has increased the variance of the phase signal, while still producing an overall reduction in the phase difference (i.e., SV₂−SV₁) standard deviation by a factor of 1.26, as shown in FIGS. 34A and 34B. The improved performance results because this correction improves the correlation between the two phases, leading to reduced variation in the phase difference. The technique works equally well in other flow conditions and on other conduit sizes.

f. Compensation to Phase Measurement for Velocity Effect

The phase measurement calculation is also affected by the velocity effect. A highly effective and simple correction factor, in radians, is of the form

${{c_{v}\left( t_{k} \right)} = {\frac{1}{\pi}\Delta \; {{SV}\left( t_{k} \right)}}},$

where −SV(t_(k)) is the relative rate of change of amplitude and may be expressed as:

${{\Delta \; {{SV}\left( t_{k} \right)}} = {\frac{{{SV}\left( t_{k + 1} \right)} - {{SV}\left( t_{k - 1} \right)}}{t_{k + 1} - t_{k - 1}} \cdot \frac{1}{{SV}\left( t_{k} \right)}}},$

where t_(k) is the completion time for the cycle for which −SV(t_(k)) is being determined, t_(k−1) is the completion time for the next cycle, and is the completion time of the previous cycle. −SV is an estimate of the rate of change of SV, scaled by its absolute value, and is also referred to as the proportional rate of change of SV.

FIGS. 35A-35E illustrate this technique. FIG. 35A shows the raw phase data from a single sensor (SV₁), after having applied the amplitude modulation corrections described above, FIG. 35B shows the correction factor in degrees calculated using the equation above, while FIG. 35C shows the resulting corrected phase. It should be noted that the standard deviation of the corrected phase has actually increased relative to the raw data. However, when the corresponding calculations take place on the other sensor (SV₂), there is an increase in the negative correlation (from −0.8 to −0.9) between the phases on the two signals. As a consequence, the phase difference calculations based on the raw phase measurements (FIG. 35D) have significantly more noise than the corrected phase measurements (FIG. 35E).

Comparison of FIGS. 35D and 35E shows the benefits of this noise reduction technique. It is immediately apparent from visual inspection of FIG. 35E that the process variable is decreasing, and that there are significant cycles in the measurement, with the cycles being attributable, perhaps, to a poorly conditioned pump. None of this is discernable from the uncorrected phase difference data of FIG. 35D.

g. Application of Sensor Level Noise Reduction

The combination of phase noise reduction techniques described above results in substantial improvements in instantaneous phase difference measurement in a variety of flow conditions, as illustrated in FIGS. 36A-36L. Each graph shows three phase difference measurements calculated simultaneously in real time by the digital Coriolis transmitter operating on a one inch conduit. The middle band 3600 shows phase data calculated using the simple time-difference technique. The outermost band 3605 shows phase data calculated using the Fourier-based technique described above.

It is perhaps surprising that the Fourier technique, which uses far more data, a more sophisticated analysis, and much more computational effort, results in a noisier calculation. This can be attributed to the sensitivity of the Fourier technique to the dynamic effects described above. The innermost band of data 3610 shows the same Fourier data after the application of the sensor-level noise reduction techniques. As can be seen, substantial noise reduction occurs in each ease, as indicated by the standard deviation values presented on each graph.

FIG. 36A illustrates measurements with no flow, a full conduit, and no pump noise. FIG. 36B illustrates measurements with no flow, a full conduit, and the pumps on. FIG. 36C illustrates measurements with an empty, wet conduit. FIG. 36D illustrates measurements at a low flow rate. FIG. 36E illustrates measurements at a high flow rate. FIG. 36F illustrates measurements at a high flow rate and an amplitude of oscillation of 0.03V. FIG. 36G illustrates measurements at a low flow rate with low aeration. FIG. 36H illustrates measurements at a low flow rate with high aeration. FIG. 36I illustrates measurements at a high flow rate with low aeration, FIG. 36J illustrates measurements at a high flow rate with high aeration. FIG. 36K illustrates measurements for an empty to high flow rate transition. FIG. 36L illustrates measurements for a high flow rate to empty transition.

2. Flowtube Level Dynamic Modelling

A dynamic model may be incorporated in two basic stages, in the first stage, the model is created using the techniques of system identification. The flowtube is “stimulated” to manifest its dynamics, while the true mass flow and density values are kept constant. The response of the flowtube is measured and used in generating the dynamic model. In the second stage, the model is applied to normal flow data. Predictions of the effects of flowtube dynamics are made for both phase and frequency. The predictions then are subtracted from the observed data to leave the residual phase and frequency, which should be due to the process alone. Each stage is described in more detail below.

a. System Identification

System identification begins with a flowtube full of water, with no flow. The amplitude of oscillation, which normally is kept constant, is allowed to vary by assigning a random setpoint between 0.05 V and 0.3 V, where 0.3 V is the usual value. The resulting sensor voltages are shown in FIG. 37A, while FIGS. 37B and 37C show, respectively, the corresponding calculated phase and frequency values. These values are calculated once per cycle. Both phase and frequency show a high degree of “structure.” Since the phase and frequency corresponding to mass flow are constant, this structure is likely to be related to flowtube dynamics. Observable variables that will predict this structure when the true phase and frequency are not known to be constant may be expressed as set forth below.

First, as noted above, −SV(t_(k)) may be expressed as:

${\Delta \; {{SV}\left( t_{k} \right)}} = {\frac{{{SV}\left( t_{k + 1} \right)} - {{SV}\left( t_{k - 1} \right)}}{t_{k + 1} - t_{k - 1}} \cdot {\frac{1}{{SV}\left( t_{k} \right)}.}}$

This expression may be used to determine −SV₁ and −SV₂.

The phase of the flowtube is related to −, which is defined as −SV₁−SV₂, while the frequency is related to −⁺, which is defined as −SV₁+−SV₂. These parameters are illustrated in FIGS. 37D and 37E. Comparing FIG. 37B to FIG. 37D and FIG. 37C to FIG. 37E shows the striking relationship between −⁻ and phase and between −⁺ and frequency.

Some correction for flowtube dynamics may be obtained by subtracting a multiple of the appropriate prediction function from the phase and/or the frequency. Improved results may be obtained using a model of the form:

y(k)+a ₁ y(k−1)+ . . . +a _(n) y(k−n)=b ₀ u(k)+b ₁ u(k−1)+ . . . +b _(m) u(k−m),

where y(k) is the output (i.e., phase or frequency) and u is the prediction function (i.e., −⁻ or −⁺). The technique of system identification suggests values for the orders n and m, and the coefficients a_(i) and b_(j), of what are in effect polynomials in time. The value of y(k) can be calculated every cycle and subtracted from the observed phase or frequency to get the residual process value.

It is important to appreciate that, even in the absence of dynamic corrections, the digital flowmeter offers very good precision over a long period of time. For example, when totalizing a batch of 200 kg, the device readily achieves a repeatability of less that 0.03%. The purpose of the dynamic modelling is to improve the dynamic precision. Thus, raw and compensated values should have similar mean values, but reductions in “variance” or “standard deviation.”

FIGS. 38A and 39A show raw and corrected frequency values. The mean values are similar, but the standard deviation has been reduced by a factor of 3.25. Though the gross deviations in frequency have been eliminated, significant “structure” remains in the residual noise. This structure appears to be unrelated to the function. The model used is a simple first order model, where m=n=1.

FIGS. 38B and 39B show the corresponding phase correction. The mean value is minimally affected, while the standard deviation is reduced by a factor of 7.9. The model orders are n=2 and m=10. Some structure appears to remain in the residual noise. It is expected that this structure is due to insufficient excitation of the phase dynamics by setpoint changes.

More effective phase identification has been achieved through further simulation of flowtube dynamics by continuous striking of the flowtube during data collection (set point changes are still carried out). FIGS. 38C and 39C show the effects of correction under these conditions. As shown, the standard deviation is reduced by a factor of 31. This more effective model is used in the following discussions.

b. Application to Flow Data

The real test of an identified model is the improvements it provides for new data. At the outset, it is useful to note a number of observations. First, the mean phase, averaged over, for example, ten seconds or more, is already quite precise. In the examples shown, phase values are plotted at 82 Hz or thereabouts. The reported standard deviation would be roughly ⅓ of the values shown when averaged to 10 Hz, and 1/9 when averages to 1 Hz. For reference, on a one inch flow tube, one degree of phase difference corresponds to about 1 kg/s flow rate.

The expected benefit of the technique is that of providing a much better dynamic response to true process changes, rather than improving average precision. Consequently, in the following examples, where the flow is non-zero, small flow step changes are introduced every ten seconds or so, with the expectation that the corrected phase will show up the step changes more clearly.

FIGS. 38D and 39D show the correction applied to a full flowtube with zero flow, just after startup. The ring-down effect characteristic of startup is clearly evident in the raw data (FIG. 38D), but this is eliminated by the correction (FIG. 39D), leading to a standard deviation reduction of a factor of 23 over the whole data set. Note that the corrected measurement closely resembles white noise, suggesting most flowtube dynamics have been captured.

FIGS. 38E and 39E show the resulting correction for a “drained” flowtube. Noise is reduced by a factor of 6.5 or so. Note, however, that there appears to be some residual structure in the noise.

The effects of the technique on low (FIGS. 38F and 39F), medium (FIGS. 38G and 39G), and high (FIGS. 38H and 39H) flow rates are also illustrated, each with step changes in flow every ten seconds. In each case, the pattern is the same: the corrected average flows (FIGS. 39F-39H) are identical to the raw average flows (FIGS. 38F-38H), but the dynamic noise is reduced considerably. In FIG. 39H, this leads to the emergence of the step changes which previously had been submerged in noise (FIG. 38H).

3. Extensions of Dynamic Monitoring and Compensation Techniques

The previous sections have described a variety of techniques (physical modelling, system identification, heuristics) used to monitor and compensate for different aspects of dynamic behavior (frequency and phase noise caused by amplitude modulation, velocity effect, flowtube dynamics at both the sensor and the flowtube level). By natural extension, similar techniques well-known to practitioners of control and/or instrumentation, including those of artificial intelligence, neural networks, fuzzy logic, and genetic algorithms, as well as classical modelling and identification methods, may be applied to these and other aspects of the dynamic performance of the meter. Specifically, these might include monitoring and compensation for frequency, amplitude and/or phase variation at the sensor level, as well as average frequency and phase difference at the flowtube level, as these variations occur within each measurement interval, as well as the time between measurement intervals (where measurement intervals do not overlap).

This technique is unusual in providing both reduced noise and improved dynamic response to process measurement changes. As such, the technique promises to be highly valuable in the context of flow measurement.

I. Aeration (Two-Phase Flow)

The digital flowmeter provides improved performance in the presence of aeration (also known as two-phase flow) in the conduit. Aeration causes energy losses in the conduit that can have a substantial negative impact on the measurements produced by a mass flowmeter and can result in stalling of the conduit. Experiments have shown that the digital flowmeter has substantially improved performance in the presence of aeration relative to traditional, analog flowmeters. This performance improvement may stem from the meter's ability to provide a very wide gain range, to employ negative feedback, to calculate measurements precisely at very low amplitude levels, and to compensate for dynamic effects such as rate of change of amplitude and flowtube dynamics. The performance improvement also may stem from the meter's use of a precise digital amplitude control algorithm.

The digital flowmeter detects the onset of aeration when the required driver gain rises simultaneously with a drop in apparent fluid density. The digital flowmeter then may directly respond to detected aeration. In general, the meter monitors the presence of aeration by comparing the observed density of the material flowing through the conduit (i.e., the density measurement obtained through normal measurement techniques) to the known, non-aerated density of the material. The controller determines the level of aeration based on any difference between the observed and actual densities. The controller then corrects the mass flow measurement accordingly.

The controller determines the non-aerated density of the material by monitoring the density over time periods in which aeration is not present (i.e., periods in which the density has a stable value). Alternatively, a control system to which the controller is connected may provide the non-aerated density as an initialization parameter.

In one implementation, the controller uses three corrections to account for the effects of aeration: bubble effect correction, damping effect correction, and sensor imbalance correction. FIGS. 40A-40H illustrate the effects of the correction procedure.

FIG. 40A illustrates the error in the phase measurement as the measured density decreases (i.e., as aeration increases) for different mass flow rates, absent aeration correction. As shown, the phase error is negative and has a magnitude that increases with increasing aeration. FIG. 40B illustrates that the resulting mass flow error also is negative. It also is significant to note that the digital flowmeter operates at high levels of aeration. By contrast, as indicated by the vertical bar 4000, traditional analog meters tend to stall in the presence of low levels of aeration.

A stall occurs when the flowmeter is unable to provide a sufficiently large driver gain to allow high drive current at low amplitudes of oscillation. If the level of damping requires a higher driver gain than can be delivered by the flowtube in order to maintain oscillation at a certain amplitude, then insufficient drive energy is supplied to the conduit. This results in a drop in amplitude of oscillation, which in turn leads to even less drive energy supplied due to the maximum gain limit. Catastrophic collapse results, and flowtube oscillation is not possible until the damping reduces to a level at which the corresponding driver gain requirement can be supplied by the flowmeter.

The bubble effect correction is based on the assumption that the mass flow decreases as the level of aeration, also referred to as the void fraction, increases. Without attempting to predict the actual relationship between void fraction and the bubble effect, this correction assumes, with good theoretical justification, that the effect on the observed mass flow will be the same as the effect on the observed density. Since the true fluid density is known, the bubble effect correction corrects the mass flow rate by the same proportion. This correction is a linear adjustment that is the same for all flow rates. FIGS. 40C and 40B illustrate, respectively, the residual phase and mass flow errors after correction for the bubble effect. As shown, the residual errors are now positive and substantially smaller in magnitude than the original errors.

The damping factor correction accounts for damping of the conduit motion due to aeration. In general, the damping factor correction is based on the following relationship between the observed phase, φ_(obs), and the actual phase, φ_(true):

${\phi_{obs} = {\phi_{true}\left( {1 + \frac{k\; \lambda^{2}\phi_{true}}{f^{2}}} \right)}},$

where Σ is a damping coefficient and k is a constant. FIG. 40E illustrates the damping correction for different mass flow rates and different levels of aeration. FIG. 40F illustrates the residual phase error after correction for damping. As shown, the phase error is reduced substantially relative to the phase error remaining after bubble effect correction.

The sensor balance correction is based on density differences between different ends of the conduit. As shown in FIG. 41, a pressure drop between the inlet and the outlet of the conduit results in increasing bubble sizes from the inlet to the outlet. Since material flows serially through the two loops of the conduit, the bubbles at the inlet side of the conduit (i.e., the side adjacent to the first sensor/driver pair) will be smaller than the bubbles at the outlet side of the conduit (i.e., the side adjacent to the second sensor/driver pair). This difference in bubble size results in a difference in mass and density between the two ends of the conduit. This difference is reflected in the sensor signals (SV₁ and SV₂). Accordingly, the sensor balance correction is based on a ratio of the two sensor signals.

FIG. 40G illustrates the sensor balance correction for different mass flow rates and different levels of aeration. FIG. 40H illustrates the residual phase error after applying the sensor balance correction. At low flow rates and low levels of aeration, the phase error is improved relative to the phase error remaining after damping correction.

Other correction factors also may be used. For example, the phase angle of each sensor signal may be monitored. In general, the average phase angle for a signal should be zero. However, the average phase angle tends to increase with increasing aeration. Accordingly, a correction factor could be generated based on the value of the average phase angle. Another correction factor could be based on the temperature of the conduit.

In general, application of the correction factors tends to keep the mass flow errors at one percent or less. Moreover, these correction factors appear to be applicable over a wide range of flows and aeration levels.

J. Setpoint Adjustment

The digital flowmeter provides improved control of the setpoint for the amplitude of oscillation of the conduit. In an analog meter, feedback control is used to maintain the amplitude of oscillation of the conduit at a fixed level corresponding to a desired peak sensor voltage (e.g., 0.3 V). A stable amplitude of oscillation leads to reduced variance in the frequency and phase measurements.

In general, a large amplitude of oscillation is desirable, since such a large amplitude provides a large Coriolis signal for measurement purposes. A large amplitude of oscillation also results in storage of a higher level of energy in the conduit, which provides greater robustness to external vibrations.

Circumstances may arise in which it is not possible to maintain the large amplitude of oscillation due to limitations in the current that can be supplied to the drivers. For example, in one implementation of an analog transmitter, the current is limited to 100 mA for safety purposes. This is typically 5-10 times the current needed to maintain the desired amplitude of oscillation. However, if the process fluid provides significant additional damping (e.g., via two-phase flow), then the optimal amplitude may no longer be sustainable.

Similarly, a low-power flowmeter, such as the two-wire meter described below, may have much less power available to drive the conduit. In addition, the power level may vary when the conduit is driven by capacitive discharge.

Referring to FIG. 42, a control procedure 4200 implemented by the controller of the digital flowmeter may be used to select the highest sustainable setpoint given a maximum available current level. In general, the procedure is performed each time that a desired drive current output is selected, which typically is once per cycle, or once every half-cycle if interleaved cycles are used.

The controller starts by setting the setpoint to a default value (e.g., 0.3 V) and initializing filtered representations of the sensor voltage (filtered_SV) and the drive current (filtered_DC) (step 4205). Each time that the procedure is performed, the controller updates the filtered values based on current values for the sensor voltage (SV) and drive current (DC) (step 4210). For example, the controller may generate a new value for filtered_SV as the sum of ninety nine percent of filtered_SV and one percent of SV.

Next, the controller determines whether the procedure has been paused to provide time for prior setpoint adjustments to take effect (step 4215). Pausing of the procedure is indicated by a pause cycle count having a value greater than zero. If the procedure is paused, the controller performs no further actions for the cycle and decrements the pause cycle count (step 4220).

If the procedure has not been paused, the controller determines whether the filtered drive current exceeds a threshold level (step 4225). In one implementation, the threshold level is ninety five percent of the maximum available current. If the current exceeds the threshold, the controller reduces the setpoint (step 4230). To allow time for the meter to settle after the setpoint change, the controller then implements a pause of the procedure by setting the pause cycle count equal to an appropriate value (e.g., 100) (step 4235).

If the procedure has not been paused, the controller determines whether the filtered drive current is less than a threshold level (step 4240) and the setpoint is less than a maximum permitted setpoint (step 4245). In one implementation, the threshold level equals seventy percent of the maximum available current, if both conditions are met, the controller determines a possible new setpoint (step 4250). In one implementation, the controller determines the new setpoint as eighty percent of the maximum available current multiplied by the ratio of filtered_SV to filtered_DC. To avoid small changes in the setpoint chattering), the controller then determines whether the possible new setpoint exceeds the current setpoint by a sufficient amount (step 4255). In one implementation, the possible new setpoint must exceed the current setpoint by 0.02 V and by ten percent.

If the possible new setpoint is sufficiently large, the controller determines if it is greater than the maximum permitted setpoint (step 4260). If so, the controller sets the setpoint equal to the maximum permitted setpoint (step 4265). Otherwise, the controller sets the setpoint equal to the possible new setpoint (step 4270). The controller then implements a pause of the procedure by setting the pause cycle count equal to an appropriate value (step 4235).

FIGS. 43A-43C illustrate operation of the setpoint adjustment procedure. As shown in FIG. 43C, the system starts with a setpoint of 0.3 V. At about eight seconds of operation, aeration results in a drop in the apparent density of the material in the conduit (FIG. 43A). Increased damping accompanying the aeration results in an increase in the drive current (FIG. 43B) and increased noise in the sensor voltage (FIG. 43C). No changes are made at this time, since the meter is able to maintain the desired setpoint.

At about fifteen seconds of operation, aeration increases and the apparent density decreases further (FIG. 43). At this level of aeration, the driver current (FIG. 43B) reaches a maximum value that is insufficient to maintain the 0.3 V setpoint. Accordingly, the sensor voltage drops to 0.26 V (FIG. 43C), the voltage level that the maximum driver current is able to maintain. In response to this condition, the controller adjusts the setpoint (at about 28 seconds of operation) to a level (0.23 V) that does not require generation of the maximum driver current.

At about 38 seconds of operation, the level of aeration decreases and the apparent density increases (FIG. 43A). This results in a decrease in the drive current (FIG. 43B). At 40 seconds of operation, the controller responds to this condition by increasing the setpoint (FIG. 43C). The level of aeration decreases and the apparent density increases again at about 48 seconds of operation, and the controller responds by increasing the setpoint to 0.3 V.

K. Performance Results

The digital flowmeter has shown remarkable performance improvements relative to traditional analog flowmeters. In one experiment, the ability of the two types of meters to accurately measure a batch of material was examined. In each case, the batch was fed through the appropriate flowmeter and into a tank, where the batch was weighed. For 1200 and 2400 pound batches, the analog meter provided an average offset of 500 pounds, with a repeatability of 200 pounds. By contrast, the digital meter provided an average offset of 40 pounds, with a repeatability of two pounds, which clearly is a substantial improvement.

In each ease, the conduit and surrounding pipework were empty at the start of the batch. This is important in many batching applications where it is not practical to start the batch with the conduit full. The batches were finished with the flowtube full. Some positive offset is expected because the flowmeter is measuring the material needed to fill the pipe before the weighing tank starts to be filled. Delays in starting up, or offsets caused by aerated flow or low amplitudes of oscillation, are likely to introduce negative offsets. For real hatching applications, the most important issue is the repeatability of the measurement.

The results show that with the analog flowmeter there are large negative offsets and repeatability of only 200 pounds. This is attributable to the length of time taken to startup after the onset of flow (during which no flow is metered), and measurement errors until full amplitude of oscillation is achieved. By comparison, the digital flowmeter achieves a positive offset, which is attributable to filling up of the empty pipe, and a repeatability of two pounds.

Another experiment compared the general measurement accuracy of the two types of meters. FIG. 44 illustrates the accuracy and corresponding uncertainty of the measurements produced by the two types of meters at different percentages of the meters' maximum recommended flow rate. At high flow rates (i.e., at rates of 25% or more of the maximum rate), the analog meter produces measurements that correspond to actual values to within 0.15% or less, compared to 0.005% or less for the digital meter. At lower rates, the offset of the analog meter is on the order of 1.5%, compared to 0.25% for the digital meter.

L. Self-Validating Meter

The digital flowmeter may used in a control system that includes self-validating sensors. To this end, the digital flowmeter may be implemented as a self-validating meter. Self-validating meters and other sensors are described in U.S. Pat. No. 5,570,300, titled “SELF-VALIDATING SENSORS”, which is incorporated by reference.

In general, a self-validating meter provides, based on all information available to the meter, a best estimate of the value of a parameter (e.g., mass flow) being monitored. Because the best estimate is based, in part, on nonmeasurement data, the best estimate does not always conform to the value indicated by the current, possibly faulty, measurement data. A self-validating meter also provides information about the uncertainty and reliability of the best estimate, as well as information about the operational status of the sensor. Uncertainty information is derived from known uncertainty analyses and is provided even in the absence of faults.

In general, a self-validating meter provides four basic parameters: a validated measurement value (VMV), a validated uncertainty (VU), an indication (MV status) of the status under which the measurement was generated, and a device status. The VMV is the meter's best estimate of the value of a measured parameter. The VU and the MV status are associated with the VMV. The meter produces a separate VMV, VU and MV status for each measurement. The device status indicates the operational status of the meter.

The meter also may provide other information. For example, upon a request from a control system, the meter may provide detailed diagnostic information about the status of the meter. Also, when a measurement has exceeded, or is about to exceed, a predetermined limit, the meter can send an alarm signal to the control system. Different alarm levels can be used to indicate the severity with which the measurement has deviated from the predetermined value.

VMV and VU are numeric values. For example, VMV could be a temperature measurement valued at 200 degrees and VU, the uncertainty of VMV, could be 9 degrees. In this case, there is a high probability (typically 95%) that the actual temperature being measured falls within an envelope around VMV and designated by VU (i.e., from 191 degrees to 209 degrees).

The controller generates VMV based on underlying data from the sensors. First, the controller derives a raw measurement value (RMV) that is based on the signals from the sensors. In general, when the controller detects no abnormalities, the controller has nominal confidence in the RMV and sets the VMV equal to the RMV. When the controller detects an abnormality in the sensor, the controller does not set the VMV equal to the RMV. Instead, the controller sets the VMV equal to a value that the controller considers to be a better estimate than the RMV of the actual parameter.

The controller generates the VU based on a raw uncertainty signal (RU) that is the result of a dynamic uncertainty analysis of the RMV. The controller performs this uncertainty analysis during each sampling period. Uncertainty analysis, originally described in “Describing Uncertainties in Single Sample Experiments,” S. J. Kline &. F. A. McClintock, Mech. Eng., 75, 3-8 (1953), has been widely applied and has achieved the status of an international standard for calibration. Essentially, an uncertainty analysis provides an indication of the “quality” of a measurement. Every measurement has an associated error, which, of course, is unknown. However, a reasonable limit on that error can often be expressed by a single uncertainty number (ANSI/ASME PTC 19.1-1985 Part 1, Measurement Uncertainty: Instruments and Apparatus).

As described by Kline & McClintock, for any observed measurement M, the uncertainty in M, w_(M), can be defined as follows:

M _(true) ε[M−w _(M) ,M+w _(M)]

where M is true (M_(true)) with a certain level of confidence (typically 95%). This uncertainty is readily expressed in a relative form as a proportion of the measurement (i.e. w_(M)/M).

In general, the VII has a non-zero value even under ideal conditions (i.e., a faultless sensor operating in a controlled, laboratory environment). This is because the measurement produced by a sensor is never completely certain and there is always some potential for error. As with the VMV, when the controller detects no abnormalities, the controller sets the VU equal to the RU. When the controller detects a fault that only partially affects the reliability of the RMV, the controller typically performs a new uncertainty analysis that accounts for effects of the fault and sets the VU equal to the results of this analysis. The controller sets the VU to a value based on past performance when the controller determines that the RMV bears no relation to the actual measured value.

To ensure that the control system uses the VMV and the VU properly, the MV status provides information about how they were calculated. The controller produces the VMV and the VU under all conditions—even when the sensors are inoperative. The control system needs to know whether VMV and VU are based on “live” or historical data. For example, if the control system were using VMV and VU in feedback control and the sensors were inoperative, the control system would need to know that VMV and VU were based on past performance.

The MV status is based on the expected persistence of any abnormal condition and on the confidence of the controller in the RMV. The four primary states for MV status are generated according to Table 1.

TABLE 1 Expected Confidence Persistence in RMV MV Status not applicable nominal CLEAR not applicable reduced BLURRED Short zero DAZZLED Long zero BLIND

A CLEAR MV status occurs when RMV is within a normal range for given process conditions. A DAZZLED MV status indicates that RMV is quite abnormal, but the abnormality is expected to be of short duration. Typically, the controller sets the MV status to DAZZLED when there is a sudden change in the signal from one of the sensors and the controller is unable to clearly establish whether this change is due to an as yet undiagnosed sensor fault or to an abrupt change in the variable being measured, A BLURRED MV status indicates that the RMV is abnormal but reasonably related to the parameter being measured.

For example, the controller may set the MV status to BLURRED when the RMV is a noisy signal. A BLIND MV status indicates that the RMV is completely unreliable and that the fault is expected to persist.

Two additional states for the MV status are UNVALIDATED and SECURE. The MV status is UNVALIDATED when the controller is not performing validation of VMV. MV status is SECURE when VMV is generated from redundant measurements in which the controller has nominal confidence.

The device status is a generic, discrete value summarizing the health of the meter. It is used primarily by fault detection and maintenance routines of the control system. Typically, the device status 32 is in one of six states, each of which indicates a different operational status for the meter. These states are: GOOD, TESTING, SUSPECT, IMPAIRED, BAD, or CRITICAL, A GOOD device status means that the meter is in nominal condition. A TESTING device status means that the meter is performing a self check, and that this self check may be responsible for any temporary reduction in measurement quality. A SUSPECT device status means that the meter has produced an abnormal response, but the controller has no detailed fault diagnosis. An IMPAIRED device status means that the meter is suffering from a diagnosed fault that has a minor impact on performance. A BAD device status means that the meter has seriously malfunctioned and maintenance is required. Finally, a CRITICAL device status means that the meter has malfunctioned to the extent that the meter may cause (or have caused) a hazard such as a leak, fire, or explosion.

FIG. 45 illustrates a procedure 4500 by which the controller of a self-validating meter processes digitized sensor signals to generate drive signals and a validated mass flow measurement with an accompanying uncertainty and measurement status. Initially, the controller collects data from the sensors (step 4505). Using this data, the controller determines the frequency of the sensor signals (step 4510). If the frequency falls within an expected range (step 4515), the controller eliminates zero offset from the sensor signals (step 4520), and determines the amplitude (step 4525) and phase (step 4530) of the sensor signals. The controller uses these calculated values to generate the drive signal (step 4535) and to generate a raw mass flow measurement and other measurements (step 4540).

If the frequency does not fall within an expected range (step 4515), then the controller implements a stall procedure (step 4545) to determine whether the conduit has stalled and to respond accordingly. In the stall procedure, the controller maximizes the driver gain and performs a broader search for zero crossings to determine whether the conduit is oscillating at all.

If the conduit is not oscillating correctly (i.e., if it is not oscillating, or if it is oscillating at an unacceptably high frequency (e.g., at a high harmonic of the resonant frequency)) (step 4550), the controller attempts to restart normal oscillation (step 4555) of the conduit by, for example, injecting a square wave at the drivers. After attempting to restart oscillation, the controller sets the MV status to DAZZLED (step 4560) and generates null raw measurement values (step 4565), if the conduit is oscillating correctly (step 4550), the controller eliminates zero offset (step 4520) and proceeds as discussed above.

After generating raw measurement values (steps 4540 or 4565), the controller performs diagnostics (step 4570) to determine whether the meter is operating correctly (step 4575). (Note that the controller does not necessarily perform these diagnostics during every cycle.)

Next, the controller performs an uncertainty analysis (step 4580) to generate a raw uncertainty value. Using the raw measurements, the results of the diagnostics, and other information, the controller generates the VMV, the VU, the MV status, and the device status (step 458). Thereafter, the controller collects a new set of data and repeats the procedure. The steps of the procedure 4500 may be performed serially or in parallel, and may be performed in varying order.

In another example, when aeration is detected, the mass flow corrections are applied as described above, the MV status becomes blurred, and the uncertainty is increased to reflect the probable error of the correction technique. For example, for a flowtube operating at 50% flowrate, under normal operating conditions, the uncertainty might be of the order of 0.1-0.2% of flowrate. If aeration occurs and is corrected for using the techniques described above, the uncertainty might be increased to perhaps 2% of reading. Uncertainty values should decrease as understanding of the effects of aeration improves and the ability to compensate for aeration gets better. In batch situations, where flow rate uncertainty is variable (e.g. high at start/end if batching from/to empty, or during temporary incidents of aeration or cavitation), the uncertainty of the batch total will reflect the weighted significance of the periods of high uncertainty against the rest of the batch with nominal low uncertainty. This is a highly useful quality metric in fiscal and other metering applications.

M. Two Wire Flowmeter

As shown in FIG. 46, the techniques described above may be used to implement a “two-wire” Coriolis flowmeter 4600 that performs bidirectional communications on a pair of wires 4605. A power circuit 4610 receives power for operating a digital controller 4615 and for powering the driver(s) 4620 to vibrate the conduit 4625. For example, the power circuit may include a constant output circuit 4630 that provides operating power to the controller and a drive capacitor 4635 that is charged using excess power. The power circuit may receive power from the wires 4605 or from a second pair of wires. The digital controller receives signals from one or more sensors 4640.

When the drive capacitor is suitably charged, the controller 4615 discharges the capacitor 4635 to drive the conduit 4625. For example, the controller may drive the conduit once during every 10 cycles. The controller 4615 receives and analyzes signals from the sensors 4640 to produce a mass flow measurement that the controller then transmits on the wires 4605.

N. Batching from Empty

The digital mass flowmeter 100 provides improved performance in dealing with a challenging application condition that is referred to as batching from empty. There are many processes, particularly in the food and petrochemical industries, where the high accuracy and direct mass-flow measurement provided by Coriolis technology is beneficial in the metering of batches of material. In many cases, however, ensuring that the flowmeter remains full of fluid from the start to the end of the batch is not practical, and is highly inefficient. For example, in filling or emptying a tanker, air entrainment is difficult to avoid. In food processing, hygiene regulations may require pipes to be cleaned out between batches.

In conventional Coriolis meters, batching from empty may result in large errors. For example, hydraulic shock and a high gain requirement may be caused by the onset of flow in an empty flowtube, leading to large measurement errors and stalling.

The digital mass flowmeter 100 is robust to the conditions experienced when batching from empty. More specifically, the amplitude controller has a rapid response; the high gain range prevents flowtube stalling; measurement data can be calculated down to 0.1% of the normal amplitude of oscillation; and there is compensation for the rate of change of amplitude.

These characteristics are illustrated in FIGS. 47A-47C, which show the response of the digital mass flowmeter 100 driving a wet and empty 25 mm flowtube during the first seconds of the onset of full flow. As shown in FIG. 47A, the drive gain required to drive the wet and empty tube prior to the onset of flow (at about 4.0 seconds) has a value of approximately 0.1, which is higher than the value of approximately 0.034 needed for a full flowtube. The onset of flow is characterized by a substantial increase in gain and a corresponding drop in amplitude of oscillation. With reference to FIG. 47B, at about 1.0 second after initiation, the selection of a reduced setpoint assists in the stabilization of amplitude while the full flow regime is established. After about 2.75 seconds, the last of the entrained air is purged, the conventional setpoint is restored, and the drive gain assumes the nominal value of 0.034. The raw and corrected phase difference behavior is shown in FIG. 47C.

As shown in FIGS. 47A-47C, phase data is given continuously throughout the transition. In similar circumstances, the analog control system stalk, and is unable to provide measurement data until the required drive gain returns to a near-nominal value and the lengthy start-up procedure is completed. As also shown, the correction for the rate of change of amplitude is clearly beneficial, particularly after 1.0 seconds. Oscillations in amplitude result in substantial swings in the Fourier-based and time-based calculations of phase, but these are substantially reduced in the corrected phase measurement. Even in the most difficult part of the transition, from 0.4-1.0 seconds, the correction provides some noise reduction.

Of course there are still erroneous data in this interval. For example, flow generating a phase difference in excess of about 5 degrees is physically not possible. However, from the perspective of a self validating sensor, such as is discussed above, this phase measurement still constitutes raw data that may be corrected. In some implementations, a higher level validation process may identify the data from 0.4-1.0 seconds as unrepresentative of the true process value (based on the gain, amplitude and other internal parameters), and may generate a DAZZLED mass-flow to suppress extreme measurement values.

Referring to FIG. 48A, the response of the digital mass flowmeter 100 to the onset of flow results in improved accuracy and repeatability. An exemplary flow rig 4800 is shown in FIG. 48B, in producing the results shown in FIG. 48A, as in producing the results shown in FIG. 44, fluid was pumped through a magnetic flowmeter 4810 and a Coriolis flowmeter 4820 into a weighing tank 4830, with the Coriolis flowmeter being either a digital flowmeter or a traditional analog flowmeter. Valves 4840 and 4860 were used to ensure that the magnetic flowmeter 4810 was always full, while the flowtube of the Coriolis flowmeter 4820 began each batch empty. At the start of the batch, the totalizers in the magnetic flowmeter 4810 and in the Coriolis flowmeter 4820 were reset and flow commenced. At the end of the batch, the shutoff valve 4850 was closed and the totals were frozen (hence the Coriolis flowmeter 4820 was full at the end of the batch). Three totals were recorded, with one each from the magnetic flowmeter 4810 and the Coriolis flowmeter 4820, and one from the weigh scale associated with the weighing tank 4830. These totals are not expected to agree, as there is a finite time delay before the Coriolis flowmeter 4820 and then finally the weighing tank 4830 observes fluid flow. Thus, it would be expected that the magnetic flowmeter 4810 would record the highest total flow, the Coriolis flowmeter 4820 would record the second highest total, and the weighing tank 4830 would record the lowest total.

FIG. 48A shows the results obtained from a series of trial rims using the flow rig 4800 of FIG. 48B, with each trial run transporting about 550 kg of material through the flow rig. The monitored value shown is the offset observed between the weigh scale and either the magnetic flowmeter 4810 or the Coriolis flowmeter 4820. As explained above, positive offsets are expected from both instruments. The magnetic flowmeter 4810 (always full) delivers a consistently positive offset, with a repeatability (defined here as the maximum difference in reported value for identical experiments) of 4.0 kg. The analog control system associated with the magnetic flowmeter 4810 generates large negative offsets, with a mean of −164.2 kg and a repeatability of 87.7 kg. This poor performance is attributable to the analog control system's inability to deal with the onset of flow and the varying time taken to restart the flowtube. By contrast, the digital Coriolis mass flowmeter 4820 shows a positive offset averaging 25.6 kg and a repeatability of 0.6 kg.

It would be difficult to assess the true mass-flow through the flowtube, given its initially empty state. The reported total mass falls between that of the magnetic flowmeter 4810 and the weigh scale, as expected. In an industrial application, the issue of repeatability is often of greater importance, as batch recipes are often adjusted to accommodate offsets. Of course, the repeatability of the filling process is a lower bound on the repeatability of the Coriolis flowmeter total. Similar repeatability could be achieved in an arbitrary industrial batch process. Moreover, as shown, the digital mass flowmeter 100 provides a substantial performance improvement over its analog equivalent (magnetic flowmeter 4810) under the same conditions. Again, the conclusion drawn is that the digital mass flowmeter 100 in these conditions is not a significant source of measurement error.

O. Two-Phase Flow

As discussed above with reference to FIG. 40A, two-phase flow, which may result from aeration, is another flow condition which presents difficulties for analog control systems and analog mass flowmeters. Two-phase flow may be sporadic or continuous and results when the material in the flowmeter includes a gas component and a fluid component traveling through the flowtube. The underlying mechanisms are very similar to the case of batching from empty, in that the dynamics of two-phase gas-liquid flow cause high damping. To maintain oscillation, a high drive gain is required. However, the maximum drive gain of the analog control system typically is reached at low levels of gas fraction in the two-phase material, and, as a result, the flowtube stalls.

The digital mass flowmeter 100 is able to maintain oscillation in the presence of two-phase flow. In summary, laboratory experiments conducted thus far have been unable to stall a tube of any size with any level of gas phase when controlled by the digital controller 105. By contrast, a typical analog control system stalls with about 2% gas phase.

Maintaining oscillation is only the first step in obtaining a satisfactory measurement performance from the flowmeter. As briefly discussed above, a simple model, referred to as the “bubble” model, has been developed as one technique to predict the mass-flow error.

In the “bubble” or “effective mass” model, a sphere or bubble of low density gas is surrounded by fluid of higher density. If both are subject to acceleration (for example in a vibrating tube), then the bubble moves within the fluid, causing a drop in the observed inertia of the whole system. Defining the void fraction I as the proportion of gas by volume, then the effective mass drops by a proportion R, with

$R = {\frac{2\alpha}{1 - \alpha}.}$

Applied to a Coriolis flowmeter, the model predicts that the apparent mass-flow will be less than the true mass-flow by the factor R as will, by extension, the observed density. FIG. 49 shows the observed mass-flow errors for a series of runs at different flow rates, all using a 25 mm flowtube in horizontal alignment, and a mixture of water and air at ambient temperature. The x-axis shows the apparent drop in density, rather than the void fraction. It is possible in the laboratory to calculate the void fraction, for example by measuring the gas pressure and flow rate prior to mixing with the fluid, together with the pressure of the two-phase mixture. However, in a plant, only the observed drop in density is available, and the true void fraction is not available. Note that with the analog flowmeter, air/water mixtures with a density drop of over 5% cause flowtube stalling so that no data can be collected.

The dashed line 4910 shows the relationship between mass-Bow error and density drop as predicted by the bubble model. The experimental data follow a similar set of curves, although the model almost always predicts a more negative mass-flow error. As discussed above with respect to FIG. 40A, it is possible to develop empirical corrections to the mass-flow rate based upon the apparent density as well as several other internally observed variables, such as the drive gain and the ratio of sensor voltages. It is reasonable to assume that the density of the pure fluid is known or can be learned. For example, in many applications the fluid density is relatively constant (particularly if a temperature coefficient is accommodated within the controller software).

FIG. 50 shows the corrected mass-flow measurements. The correction is based on a least squares in of several internal variables, as well as the bubble model itself. The correction process has only limited applicability, and it is less accurate for lower flow rates (the biggest errors are for 1.5-1.6 kg/second). In horizontal orientation, the gas and liquid phase begin to separate for lower flow rates, and much larger mass-flow errors are observed. In these circumstances, the assumptions of the bubble model are no longer valid. However, the correction is reasonable for higher flow rates. During on-line experimental trials, a similar correction algorithm has restricted mass flow errors to within about 2.5% of the mass flow reading.

FIG. 51 shows how a self validating digital mass flowmeter 100 responds to the onset of two-phase flow in its reporting of the mass-flow rate. The lower waveform 5110 shows an uncorrected mass-flow measurement under a two-phase flow condition, and the upper waveform 5120 shows a corrected mass-flow measurement and uncertainty bound under the same two-phase flow condition. With single phase flow (up to t=7 sec.), the mass-flow measurement is CLEAR and has a small uncertainty of about 0.2% of the mass flow reading. With the onset of two-phase flow, a number of processes become active. First, the two-phase flow is detected on the basis of the behavior of internally observed parameters. Second, a measurement correction process is applied, and the measurement status output along with the corrected measurement is set to BLURRED. Third, the uncertainty of the mass flow increases with the level of void fraction to a maximum of about 2.3% of the mass flow reading. For comparison, the uncorrected mass-flow measurement 5110 is shown directly below the corrected mass-flow measurement 5120. The user thus has the option of continuing operation with the reduced quality of the corrected mass-flow rate, switching to an alternative measurement if available, or shutting down the process.

P. Application of Neural Networks

Another technique for improving the accuracy of the mass flow measurement during two-phase flow conditions is through the use of a neural network to predict the mass-flow error and to generate an error correction factor for correcting any error in the mass-flow measurement resulting from two-phase flow effects. The correction factor is generated using internally observed parameters as inputs to the digital signal processor and the neural network, and has been observed to keep errors to within 2%. The internally observed parameters may include temperature, pressure, gain, drop in density, and apparent flow rate.

FIG. 52 shows a digital controller 5200 that can be substituted for digital controller 105 or 505 of the digital mass flowmeters 100, 500 of FIGS. 1 and 5. In this implementation of digital controller 5200, process sensors 5204 connected to the flowtube generate process signals including one or more sensor signals, a temperature signal, and one or more pressure signals (as described above). The analog process signals are converted to digital signal data by A/D converters 5206 and stored in sensor and driver signal data memory buffers 5208 for use by the digital controller 5200. The drivers 5245 connected to the flowtube generate a drive current signal and may communicate this signal to the A/D converters 5206. The drive current signal then is converted to digital data and stored in the sensor and driver signal data memory buffers 5208. Alternatively, a digital drive gain signal and a digital drive current signal may be generated by the amplitude control module 3235 and communicated to the sensor and driver signal data memory buffers 5208 for storage and use by the digital controller 5200.

The digital process sensor and driver signal data are further analyzed and processed by a sensor and driver parameters processing module 5210 that generates physical parameters including frequency, phase, current, damping and amplitude of oscillation. A raw mass-flow measurement calculation module 5212 generates a raw mass-flow measurement signal using the techniques discussed above with respect to the flowmeter 500.

A flow condition state machine 5215 receives as input the physical parameters from the sensor and driver parameters processing module 5210, the raw mass-flow measurement signal, and a density measurement 5214 that is calculated as described above. The flow condition state machine 5215 then detects a flow condition of material traveling through the digital mass flowmeter 100. In particular, the flow condition state machine 5215 determines whether the material is in a single-phase flow condition or a two-phase flow condition. The flow condition state machine 5215 also inputs the raw mass-flow measurement signal to a mass-flow measurement output block 5230.

When a single-phase flow condition is detected, the output block 5230 validates the raw mass-flow measurement signal and may perform an uncertainty analysis to generate an uncertainty parameter associated with the validated mass-flow measurement. In particular, when the state machine 5215 detects that a single-phase flow condition exists, no correction factor is applied to the raw mass-flow measurement, and the output block 5230 validates the mass-flow measurement. If the controller 5200 does not detect errors in producing the measurement, the output block 5230 may assign to the measurement the conventional uncertainty parameter associated with the fault free measurement, and may set the status flag associated with the measurement to CLEAR. If errors are detected by the controller 5200 in producing the measurement, the output block 5230 may modify the uncertainty parameter to a greater uncertainty value, and may set the status flag to another value such as BLURRED.

When the flow condition state machine 5215 detects that a two-phase flow condition exists, a two-phase flow error correction module 5220 receives the raw mass-flow measurement signal. The two-phase flow error correction module 5220 includes a neural network processor for predicting, a mass-flow error and for calculating an error correction factor. The neural network processor can be implemented in a software routine, or alternatively may be implemented as a separate programmed hardware processor. Operation of the neural network processor is described in greater detail below.

A neural network coefficients and training module 5225 stores a predetermined set of neural network coefficients that are used by the neural network processor. The neural network coefficients and training module 5225 also may perform an online training function using training data so that an updated set of coefficients can be calculated for use by the neural network. While the predetermined set of neural network coefficients are generated through extensive laboratory testing and experiments based upon known two-phase mass-flow rates, the online training function performed by module 5225 may occur at the initial commissioning stage of the flowmeter, or may occur each time the flowmeter is initialized.

The error correction factor generated by the error correction module 5220 is input to the mass-flow measurement output block 5230. Using the raw mass-flow measurement and the error correction factor (if received from the error correction module 5220 indicating two-phase flow), the mass-flow measurement output block 5230 applies the error correction factor to the raw mass-flow measurement to generate a corrected mass-flow measurement. The measurement output block 5230 then validates the corrected mass-flow measurement, and may perform an uncertainty analysis to generate an uncertainty parameter associated with the validated mass-flow measurement. The measurement output block 5230 thus generates a validated mass-flow measurement signal that may include an uncertainty and status associated with each validated mass-flow measurement, and a device status.

The sensor parameters processing module 5210 also inputs a damping parameter and an amplitude of oscillation parameter (previously described) to an amplitude control module 5235. The amplitude control module 5235 further processes the damping parameter and the amplitude of oscillation parameter and generates digital drive signals. The digital drive signals are converted to analog drive signals by D/A converters 5240 for operating the drivers 5245 connected to the flowtube of the digital flowmeter. In an alternate implementation, the amplitude control module 5235 may process the damping parameter and the amplitude of oscillation parameter and generate analog drive signals for operating the drivers 5245 directly.

FIG. 53 shows a procedure 5250 performed by the digital controller 5200. After processing begins (step 5251), measurement signals generated by the process sensors 5204 and drivers 5245 are quantified through an analog to digital conversion process (as described above), and the memory buffers 5208 are filled with the digital sensor and driver data (step 5252). For every new processing cycle, the sensor and driver parameters processing module 5210 retrieves the sensor and driver data from the buffers 5208 and calculates sensor and driver variables from the sensor data (step 5254). In particular, the sensor and driver parameters processing module 5210 calculates sensor voltages, sensor frequencies, drive current, and drive gain.

The sensor and driver parameters processing module 5210 then executes a diagnose_flow_condition processing routine (step 5256) to calculate statistical values including the mean, standard deviation, and slope for each of the sensor and driver variables. Based upon the statistics calculated for each of the sensor and driver variables, the flow condition state machine 5215 detects transitions between one of three valid flow-condition states: FLOW_CONDITION_SHOCK, FLOW_CONDITION_HOMOGENEOUS, AND FLOW_CONDITION_MIXED.

If the state FLOW_CONDITION_SHOCK is detected (step 5258), the mass-flow measurement analysis process is not performed due to irregular sensor inputs. On exit from this condition, the processing routine starts a new cycle (step 5251). The processing routine then searches for a new sinusoidal signal to track within the sensor signal data and resumes processing. As part of this tracking process, the processing routine must find the beginning and end of the sine wave using the zero crossing technique described above. If the state FLOW_CONDITION_SHOCK is not detected, the processing routine calculates the raw mass-flow measurement of the material flowing through the flowmeter 100 (step 5260).

If two-phase flow is not detected (i.e., the state FLOW_CONDITION_HOMOGENOUS is detected) (step 5270), the material flowing through the flowmeter 100 is assumed to be a single-phase material. If so, the validated mass-flow rate is generated from the raw mass-flow measurement (step 5272) by the mass-flow measurement output block 5230. At this point, the validated mass-flow rate along with its uncertainty parameter and status flag can be transmitted to another process controller. Processing then begins a new cycle (step 5251).

If two-phase flow is detected (i.e., the state FLOW_CONDITION_MIXED is detected) (step 5270), the material flowing through the flowmeter 100 is assumed to be a two-phase material. In this case, the two-phase flow error correction module 5220 predicts the mass-flow error and generates an error correction factor using the neural network processor (step 5274). The corrected mass-flow rate is generated by the mass-flow measurement output block 5230 using the error correction factor (step 5276). A validated mass-flow rate then may be generated from the corrected mass-flow rate. At this point, the validated mass-flow rate along with its uncertainty parameter and status flag can be transmitted to another process controller. Processing then begins a new cycle (step 5251).

Referring again to FIG. 52, the neural network processor forming part of the two-phase flow error correction module 5220 is a feed-forward neural network that provides a non-parametric framework for representing a non-linear functional mapping between an input and an output space. The neural network is applied to predict mass flow errors during two-phase flow conditions in the digital mass flowmeter. Once the error is predicted by the neural network, an error correction factor is applied to the two-phase mass flow measurement to correct the errors. Thus, the system allows the errors to be predicted on-line by way of the neural network using only internally observable parameters derived from the sensor signal sensor variables, and sensor statistics.

Of the various neural network models available, the multi-layer perceptron (MLP) and the radial basis function (RBF) networks have been used for implementations of the digital flowmeter. The MLP with one hidden layer (each unit having a sigmoidal activation function) can approximate arbitrarily well any continuous mapping. Therefore, this type of neural network is suitable to model the non-linear relationship between the mass flow error of the flowmeter under two-phase flow and some of the flowmeter's internal parameters.

The network weights necessary for accomplishing the desired mapping are determined during a training or optimization process. During supervised training, the neural network is repeatedly presented with the training set (a collection of input examples x and their corresponding desired outputs d_(i)), and the weights are updated such that an error function is minimized. For the interpolation problem associated with the present technique, a suitable error function is the sum-of-squares error, which for an MLP with one output may be represented as:

$I = {{\sum\limits_{i = 1}^{P}{e_{i}(t)}^{2}} = {\sum\limits_{i = 1}^{P}\left( {d_{i} - y_{i}} \right)^{2}}}$

where d_(i) is the target corresponding to input x_(i); y_(i) is the actual neural network output to x_(i); and P is the number of examples in the training set.

An alternative neural network architecture that has been used is the RBF network. The RBF methods have their origins in techniques for performing exact interpolations of a set of data points in a multi-dimensional space. A RBF network generally has a simple architecture of two layers of weights, in which the first layer contains the parameters of the basis functions, and the second layer forms linear combinations of the activation of the basis functions to generate the outputs. This is achieved by representing the output of the network as a linear superposition of basis functions, one for each data point in the training set. In this form, training is faster than for a MLP network.

The internal sensor parameters of interest include observed density, damping, apparent flow rate, and temperature. Each of these parameters is discussed below.

1. Observed Density

The most widely used metric of two-phase flow is the void (or gas) fraction defined as the proportion of gas by volume. The equation

$R = \frac{2\alpha}{1 - \alpha}$

models the mass flow error given the void fraction. For a Coriolis mass flowmeter, the reported process fluid density provides an indirect measure of void fraction assuming the “true” liquid density in known. This reported process density is subject to errors similar to those in the mass-flow measurement in the presence of two-phase flow. These errors are highly repeatable and a drop in density is a suitable monotonic but non-linear indicator of void fraction, that can be monitored on-line within the flowmeter. It should be noted that outside of a laboratory environment, the true void fraction cannot be independently assessed, but rather, must be modeled as described above.

Knowledge of the “true” single-phase liquid density can be obtained on-line or can be configured by the user (possibly including a temperature coefficient). Both approaches have been implemented and appear satisfactory.

For the purposes of these descriptions, the drop in density will be used as the x-axis parameter in graphs showing two-phase flow behavior. It should be noted that in the 3D plots of FIGS. 54 and 56-57, which capture the results from 134 on-line experiments, a full range of density drop points is not possible at high flow rates due to air pressure limitations in the flow system rig. Also, the effects of temperature, though not shown in the graphs, have been determined experimentally.

2. Damping

Most Coriolis flowmeters use positive feedback to maintain flowtube oscillation. The sensor signals provide the frequency and phase of the flowtube oscillation, which are multiplied by a drive gain, K₀ to provide the currents supplied to the drivers 5245:

$K_{0} = {\frac{{drive}\mspace{14mu} {signal}{\mspace{11mu} \;}{out}\mspace{14mu} ({Amps})}{{sensor}\mspace{14mu} {signal}\mspace{14mu} {in}\mspace{11mu} ({Volts})} = {\frac{I_{D}}{V_{A}}.}}$

Normally, the drive gain is modified to ensure a constant amplitude of oscillation, and is roughly proportional to the damping factor of the flowtube.

One of the most characteristic features of two-phase flow is a rapid rise in damping. For example, a normally operating 25 mm flowtube has V_(A)=0.3V, I_(D)=10 mA, and hence K₀=0.033. With two-phase flow, values might be as extreme as V_(A)=0.03V, I_(D)=100 mA, and K₀=3.3, a hundred-fold increase. FIG. 54 shows how damping varies with two-phase flow.

3. Apparent Flow Rate and Temperature

As FIG. 49 shows, the mass flow error varies with the true flow rate. Temperature variation also has been observed. However, the true mass-flow rate is not available within the transmitter (or digital controller) itself when the flowmeter is subject to two-phase flow. However, observed (faulty) flow rate, as well as the temperature, are candidates as input parameters to the neural network processor.

Q. Network Training and On-Line Correction of Mass Flow Errors

Implementing the neural network analysis for the mass flow measurement error prediction includes training the neural network processor to recognize the mass flow error pattern in training experimental data, testing the performance of the neural network processor on a new set of experimental data, and on-line implementation of the neural network processor for measurement error prediction and correction.

The prediction quality of the neural network processor depends on the wealth of the training data. To collect the neural network data, a series of two-phase air/water experiments were performed using an experimental flow rig 5500 shown in FIG. 55. The flow loop includes a master meter 5510, the self validating SEVA® Coriolis flowmeter 100, and a diverter 5520 to transfer material from the flowtube to a weigh scale 5530. The Coriolis flowmeter 100 has a totalization function which can be triggered by external signals. The flow rig control is arranged so that both the flow diverter 5520 (supplying the weigh scale) and Coriolis totalization are triggered by the master meter 5510 at the beginning of the experiment, and again after the master meter 5510 has observed 100 kg of flow. The weigh scale total is used as the reference to calculate mass flow error by comparison with the totalized flow from the digital flowmeter 100, with the master meter 5510 acting as an additional check. The uncertainty of the experimental rig is estimated at about 0.1% on typical batch sizes of 100 kg. For single-phase experiments, the digital flowmeter 100 delivers mass flow totals within 0.2% of the weigh scale total. For two-phase flow experiments, air is injected into the flow after the master meter 5510 and before the Coriolis flowmeter 100. At low flow rates, density drops of up to 30% are achieved. At higher flow rates, at least 15% density drops are achieved.

At the end of each batch, the Coriolis flowmeter 100 reports the batch average of each of the following parameters: temperature, damping, density, flow rate, and the total (uncorrected) flow. These parameters are thus available for use as the input data of the neural network processor.

The output or the target of the neural network is the mass-flow error in percentage:

${{mass\_ error}\mspace{14mu} \%} = {\frac{{coriolis} - {weighscale}}{weighscale} \times 100}$

FIG. 56 shows how the mass-flow error varies with flow rate and density drop. Although the general trend follows the bubble model, there are additional features of interest. For example, for high flow rates and low density drops, the mass-flow error becomes slightly positive (by as much as 1%), while the bubble model only predicts a negative error. As is apparent from FIG. 56, in this region of the experimental space, and for this flowtube design, some other physical process is taking place to overcome the missing mass effects of two-phase flow.

The best results were obtained using only four input parameters to the neural network: temperature, damping, density drop, and apparent flow rate. Less satisfactory perhaps is the result that the best fit was achieved using the neural network on its own, rather than as a correction to the bubble model or to a simplified curve fit.

As part of the implementation, a MLP neural network was used for on-line implementation. Comparisons between RBF and MLP networks with the same data sets and inputs have shown broadly similar performance on the test set. It is thus reasonable to assume that the input set delivering the best RBF design also will deliver a good (if not the best) MLP design. The MLP neural nets were trained using a scaled conjugate gradient algorithm. The facilities of Neural Network Toolbox of the MATLAB® software package were used for neural network training. After further design options were explored, the best performance came from an 4-9-1 MLP having as inputs the temperature, damping, drop in density, and flow rate, along with the mass flow error as output.

Against a validation set, the best neural network provided mass flow rate predictions to within 2% of the true value. Routines for the detection and correction for two-phase flow have been coded and incorporated into the digital Coriolis transmitter. FIG. 57 shows the residual mass-flow error when corrected on-line over 134 new experiments. All errors are with 2%, and most are considerably less. The random scatter is due primarily to residual error in the neural network correction algorithm (as stated earlier, the uncertainty of the flow rig is 0.1%). Any obvious trend in the data would imply scope for further correction. These errors are of course for the average corrected mass flow rate (i.e. over a batch).

FIG. 58 shows how the on-line detection and correction for two-phase flow is reflected in the self-validating interface generated for the mass flow measurement. In the graph, the lower, continuous line 5810 is the raw mass flow rate. The upper line 5820 is a measurement surrounded by the uncertainty band, and represents the corrected or validated mass-flow rate. The dashed line 5830 is the mass flow rate from the master meter, which is positioned prior to the air injection point (FIG. 55).

With single-phase flow (up to t=5 sec.), the mass flow measurement has a measurement value status of CLEAR and a small uncertainty of about 0.2% of reading. Once two-phase flow is detected, the neural network correction is applied every interleaved cycle (i.e. at 180 Hz), based on the values of the internal parameters averaged (using a moving window) over the last second. During two-phase flow, the measurement value status is set to BLURRED, and the uncertainty increases to reflect the reduced accuracy of the corrected measurement. The uncorrected measurement (lower dark line) shows a large offset error of about 30%.

The master meter reading agrees with a first approximation with the corrected mass flow measurement. The apparent delay in its response to the incidence of two-phase flow is attributable to communication delays in the rig control system, and its squarewave-like response is due to the control system update rate of only once per second. Both the raw and corrected measurements from the digital transmitter show a higher degree of variation than with single phase flow. The master meter reading gives a useful measure of the water phase entering the two-phase zone, and there is a clear similarity between the master meter reading and the ‘average’ corrected reading. However, plug flow and air compressibility within the complex 3-D geometry of the flowtube may result not only in flow rate variations, but with the instantaneous mass-flow into the system being different from the mass-flow out of the system.

Using the self-validating sensor processing approach, the measurement is not simply labeled good or bad by the sensor. Instead, if a fault occurs, a correction is applied as far as possible and the quality of the resulting measurement is indicated through the BLURRED status and increased uncertainty. The user thus assesses application-specific requirements and options in order to decide whether to continue operation with the reduced quality of the corrected mass-flow rate, to switch to an alternative measurement if available, or to shut down the process. If two-phase flow is present only during part of a batch run (e.g. at the beginning or end), then there will be a proportionate weighting given to the uncertainty of the total mass of the batch.

Multiple Phase-Flows

FIG. 59 shows an example process 5900 used to determine a phase-specific property of a phase included in a multi-phase process fluid. For example, the process 5900 may be used to determine the mass flow rate and density of each phase of the multi-phase process fluid.

As described below, an apparent intermediate value is determined based on, e.g., a mass flow rate and density of the multi-phase process fluid (also referred to as the bulk mass flow rate and bulk density, respectively) as determined by, for example, a Coriolis meter. Although the Coriolis meter continues to operate in the presence of the multi-phase process fluid, the presence of the multi-phase fluid effects the motion of a flowtube (or conduit) that is part of the Coriolis meter. Thus, the outputs determined by the meter may be inaccurate because the meter operates on the assumption that the process fluid includes a single phase. These outputs may be referred to as apparent properties, or raw properties, of the multi-phase fluid. Thus, in one implementation, the apparent intermediate value is determined based on apparent or raw properties of the multi-phase fluid. Other implementations may determine the intermediate value based on a corrected form of the apparent property(ies). To correct for the inaccuracies, the apparent intermediate value is input into, e.g., a neural network to produce a corrected intermediate value that accounts for the effects of using a multi-phase process fluid. The corrected intermediate value is used to determine the mass flow rate and density of each of the phases of the multi-phase process fluid. Using an intermediate value rather than the apparent mass flow rate and density of the multi-phase process fluid may help improve the accuracy of the determination of the mass flow rate and density of each of the phases of the multi-phase process fluid.

A multi-phase process fluid is passed through a vibratable flowtube (5905). Motion is induced in the vibratable flowtube (5910). The vibratable flow tube may be, for example, the conduit 120 discussed above with respect to FIG. 1. The multi-phase process fluid also may be referred to as a multi-phase flow. The multi-phase fluid may be a two-phase fluid, a three-phase fluid, or a fluid that includes more than three phases. In general, each phase of the multi-phase fluid may be considered to be constituents or components of the multi-phase fluid. For example, a two-phase fluid may include a non-gas phase and a gas phase. The non-gas phase may be a liquid, such as oil, and the gas phase may be a gas, such as air. A three-phase fluid may include two non-gas phases and one gas phase or one non-gas phase and two gas phases. For example, the three-phase fluid may include a gas and two liquids such as water and oil. In another example, the three-phase fluid may include a gas, a liquid, and a solid (such as sand). Additionally, the multi-phase fluid may be a wet gas. While the wet gas may be any of the multi-phase fluids described above, wet gas is generally composed of more than 95% gas phase by volume. The process 5900 may be applied to any multi-phase fluid.

A first property of the multi-phase fluid may be determined based on the motion of the vibratable flowtube (5915). The first property of the multi-phase fluid may be the apparent mass flow rate and/or the apparent density of the fluid flowing through the vibratable flowtube. Thus, in the example process 5900, the first property may be the mass flow rate or the density of the multi-phase fluid. Properties determined from a multi-phase fluid may be referred to as apparent, or raw, properties as compared to true (or at lest corrected) properties of the multi-phase fluid. The apparent mass flow rate and density of the multi-phase fluid are generally inconsistent with the mass flow rate and density of each of the individual phases of the multi-phase fluid due to the effects the multi-phase fluid has on the motion of the flowtube. For example, if the multi-phase fluid has a relatively low gas volume fraction (e.g., the multi-phase fluid includes more liquid than gas), both the apparent density and the apparent mass flow rate of the multi-phase fluid obtained from the flowtube tend to be lower than the actual density and mass flowrate of the non-gas phase. Although the first property is generally an apparent property, in some implementations, the first property may be a corrected or actual property. The corrected or actual property may come, for example, from a model or a mapping.

As discussed with respect to FIG. 1, the mass flow rate is related to the motion induced in the vibratable flowtube, in particular, the mass flow rate is related to the phase and frequency behavior of the motion of the flowtube and the temperature of the flowtube. Additionally, the density of the fluid is related to the frequency of motion and temperature of the flowtube. Thus, because the fluid flowing through the flowtube includes more than one phase, the vibratable flowtube provides the mass flow rate and density of the multi-phase fluid rather than the mass flow rate and density of each phase of the multi-phase fluid. As described in more detail below, the process 5900 may be used to determine properties of each of the phases of the multi-phase fluid.

In general, to determine the properties of the individual phases in the multi-phase fluid, additional information (e.g., the known densities of the materials in the individual phases) or additional measurements (e.g., pressure of the multi-phase fluid or the watercut of the multi-phase fluid) may be needed at times. However, the properties of the multi-phase fluid measured by the meter are typically determined by modification or correction to conventional single-phase measurement techniques because of the effects the multi-phase flow has on the flowtube as compared to single-phase flow.

Thus, in some implementations, in addition to properties determined based on the motion of the conduit, such as the first property discussed above, additional or “external” properties of the multi-phase fluid such as temperature, pressure, and watercut may be measured and used in the process 5900, e.g., as additional inputs to the mapping or to help in determining the flowrates of the individual components of the multi-phase fluid. The additional properties may be measured by a device other than the flowmeter. For example, the watercut of the multi-phase fluid, which represents the portion of the multi-phase fluid that is water, may be determined by a watercut meter. The additional property also may include a pressure associated with the flowtube. The pressure associated with the flowtube may be, for example, a pressure of the multi-phase process fluid at an inlet of the flowtube and/or a differential pressure across the flowtube.

An apparent intermediate value associated with the multi-phase process fluid is determined based on the first property (5920). In some implementations, a second property of the multi-phase fluid also may be determined based on the motion of the conduit. For example, in such an implementation, the apparent mass flowrate of the multi-phase fluid and the apparent density of the multi-phase fluid may be determined based on the motion of the conduit, and both, of these apparent properties may be used to determine one or more apparent, intermediate values (such as liquid volume fraction and the volumetric flowrate or the gas Froude number and non-gas Froude number, as described below). In some implementations, the apparent intermediate values may be intermediate values based on one or more corrected or actual properties.

In general, the apparent intermediate value (or values) is a value related to the multi-phase fluid that reflects inaccuracies resulting from the inclusion of more than one phase in the multi-phase fluid. The apparent intermediate value may be, for example, a volume fraction of the multi-phase process fluid. The volume fraction may be a liquid volume fraction that specifies the portion of the multi-phase fluid that is a non-gas. The volume fraction also may be a gas volume fraction that specifies the portion of the multi-phase fluid that is a gas. In general, the volume fraction is a dimensionless quantity that may be expressed as a percentage. The gas volume fraction also may be referred to as a void fraction. If the multi-phase fluid includes liquids and gases, the liquid and gas volume fractions add up to 100%. In other implementations, the apparent intermediate value may be a volumetric flow rate of the multi-phase fluid.

In another implementation, the apparent intermediate values may include a non-gas Froude number and a gas Froude number. Froude numbers are dimensionless quantities that may represent a resistance of an object moving through a fluid and which may be used to characterize multi-phase fluids. In this implementation, the apparent intermediate value may be a non-gas Froude number and/or a gas Froude number. The apparent gas Froude number may be calculated using the following equation, where m_(g) ^(a) is the apparent gas mass flow rate, ρ_(g) is an estimate of the density of the gas phase based on the ideal gas laws, ρ_(l) is an estimate of the density of the liquid in the non-gas phase of the multi-phase fluid, A is the cross-sectional area of the flowtube, D is the diameter of the flowtube, and g is the acceleration due to gravity:

${Fr}_{g}^{a} = {{\frac{m_{g}^{a}}{\rho_{g}A\sqrt{g \cdot D}}\sqrt{\frac{\rho_{g}}{\rho_{l} - \rho_{g}}}} = {K \cdot V_{g}^{a} \cdot \sqrt{\frac{\rho_{g}}{\rho_{l} - \rho_{g}}}}}$

where

${K = \frac{1}{\sqrt{g \cdot D}}},$

the Apparent Gas Velocity

$V_{g}^{a} = {\frac{m_{g}^{a}}{\rho_{g}A}.}$

Similarly, the non-gas Froude number (which may be a liquid Froude number) may be calculated using the following equation, where m_(l) ^(a) is the apparent liquid mass flow rate:

${Fr}_{l}^{a} = {{\frac{m_{l}^{a}}{\rho_{l}A\sqrt{g \cdot D}}\sqrt{\frac{\rho_{l}}{\rho_{l} - \rho_{g}}}} = {K \cdot V_{l}^{a} \cdot {\sqrt{\frac{\rho_{l}}{\rho_{l} - \rho_{g}}}.}}}$

As discussed in more detail below, the apparent intermediate value is input into a mapping that defines a relationship between the apparent intermediate value and a corrected intermediate value. The mapping may be, for example, a neural network, a polynomial, a function, or any other type of mapping. Prior to inputting the apparent intermediate value into the mapping, the apparent intermediate value may be filtered or conditioned to reduce measurement and process noise. For example, linear filters may be applied to the apparent intermediate value to reduce measurement noise. The time constant of the linear filter may be set to a value that reflects the response time of the measurement instrumentation (e.g., 1 second) such that the filter remains sensitive to actual changes in the fluid flowing through the flowtube (such as slugs of non-gas fluid) while also being able to reduce measurement noise.

The development of a mapping for correcting or improving a multiphase measurement involves the collection of data under experimental conditions, where the true or reference measurements are provided by additional calibrated instrumentation. Generally, it is not practical to carry out experiments covering all conceivable multi-phase conditions, either due to limitations of the test facility, and/or the cost and time associated with carrying out possibly thousands of experiments. Additionally, it is rarely possible to maintain multiphase flow conditions exactly constant for any extended period of time, due to the inherently unstable flow conditions that occur within multiphase conditions. Accordingly, it is usually necessary to calculate the average values of all relevant parameters, including apparent and true or reference parameter values, over the duration of each experiment, which may typically be of 30 s to 120 s duration. Thus, the mapping may be constructed from experimental data where each data point is derived from the average of for example 30 s to 120 s duration of data.

Difficulties might arise when applying the resulting mapping in the meter during multiphase flow in real time, whereby the particular parameter values observed within the meter are not included in the mapping provided from the previously collected experimental data. There are two primary ways in which this may occur. In the first instance, although the conditions experienced by the meter, averaged over a timescale of about 15 to 120 seconds, do correspond to conditions covered by the mapping, the instantaneous parameter values may fall outside of the region, due to measurement noise and or instantaneous variations in actual conditions due to the instabilities inherent in multiphase flow. As described above, this effect can to some extent be reduced by time-averaging or filtering the parameters used as inputs into the mapping function, though there is a tradeoff between the noise reduction effects of such filtering and the responsiveness of the meter to actual changes in conditions within the multiphase flow. Alternatively, averaged parameter values may fall outside of the mapping because, for instance, it has not been economically viable to cover all possible multiphase conditions during the experimental stage.

It may not be beneficial to apply a mapping function (whether neural net, polynomial or other function) to data that falls outside of the region for which the mapping was intended. Application of the mapping to such data may result in poor quality measurements being generated. Accordingly, jacketing procedures may be applied to ensure that the behavior of the mapping procedure is appropriate for parameter values outside the mapped region, irrespective of the reasons for the parameters falling outside the mapped region. Data that is included in the region may be referred to as suitable data.

Thus, the apparent intermediate value may be “jacketed” prior to inputting the apparent intermediate value into the mapping. For implementations that include one input to the mapping, the region of suitable data may be defined by one or more limits, a range, or a threshold. In other implementations, there may be more than one input to the mapping. In these implementations, the region of suitable data may be defined by a series of lines, curves, or surfaces. Accordingly, as the number of inputs to the mapping increases, defining the region of suitable data becomes more complex. Thus, it may be desirable to use fewer inputs to the mapping. The gas and liquid Froude numbers described above are an example of apparent intermediate values that may be input into the mapping without additional inputs. Thus, use of the gas and non-gas Froude numbers may help to reduce the number of inputs into the mapping, which also may help reduce the complexity of the jacketing process. Additionally, using fewer inputs to the mapping may result in a simpler mapping, which may help reduce the computational resources used by the mapping and help increase the speed of determining corrected intermediate values based on the mapping.

An apparent intermediate value having a value that is outside of the defined region may be determined to be unsuitable for input to the mapping. In general, rules are defined to correct an apparent intermediate value that is determined to be outside of the defined region. For example, an apparent intermediate value that is outside of the defined region may be ignored by the mapping (e.g., the apparent intermediate value is not corrected by the mapping), the apparent intermediate value may not be input to the mapping at all, a fixed correction may be applied to the apparent intermediate value rather than a correction determined by the mapping, or the correction corresponding to the correction that would apply to the value closest to the apparent intermediate value may be applied. Other rules for correcting an apparent intermediate value that is outside of the defined region may be implemented. In general, the jacketing is specific to a particular mapping and is defined for each mapping.

A corrected intermediate value based on a mapping between the apparent intermediate value and the corrected intermediate value is determined (5925). The mapping may be a neural network, a statistical model, a polynomial, a function, or any other type of mapping. The neural network or other mapping may be trained with data obtained from a multi-phase fluid for which values of the constituent phases are known. Similar to the jacketing described above with respect to (5920), the corrected apparent value may be jacketed, or otherwise checked, prior to using it in further processing. A phase-specific property of the multi-phase process fluid may be determined based on the corrected intermediate value (5930). Using one or more of the apparent intermediate values discussed above rather than a value directly from the flowtube (e.g., mass flow rate of the multi-phase liquid) may improve the accuracy of the process 5900. The phase-specific property may be, for example, a mass flow rate and/or a density of the non-gas and gas phases of the multi-phase fluid.

The example process described with respect to FIG. 59 may be implemented in software or hardware. FIGS. 60 and 61 describe an example implementation. With respect to FIGS. 60 and 61, optional components are shown with dashed-lines. Specifically, FIGS. 60 and 61 demonstrate the application of a digital flowmeter to a fluid having multiple phases that are either expected to be encountered frequently (such as a batch process described above) or with a fluid flow having a heterogeneous mixture of constituents (one or more gas constituents and/or one or more liquid constituents).

FIG. 60 shows a digital controller 6200 that can be substituted for digital controller 105 or 505 of the digital mass flowmeters 100, 500 of FIGS. 1 and 5. In this implementation of digital controller 6200, process sensors 6204 connected to the flowtube generate process signals including one or more sensor signals, a temperature signal, and one or more pressure signals (as described above). The analog process signals are converted to digital signal data by A/D converters 6206 and stored in sensor and driver signal data memory buffers 6208 for use by the digital controller 6200. The drivers 6245 connected to the flowtube generate a drive current signal and may communicate this signal to the A/D converters 6206. The drive current signal then is converted to digital data and stored in the sensor and driver signal data memory buffers 6208. Alternatively, a digital drive gain signal and a digital drive current signal may be generated by the amplitude control module 6235 and communicated to the sensor and driver signal data memory buffers 6208 for storage and use by the digital controller 6200.

The digital process sensor and driver signal data are further analyzed and processed by a sensor and driver parameters processing module 6210 that generates physical parameters including frequency, phase, current, damping and amplitude of oscillation. A raw mass-flow measurement calculation module 6212 generates a raw mass-flow measurement signal using the techniques discussed above with respect to the flowmeter 500.

Rather than include a dedicated flow condition state machine, such as 5215 discussed in reference to flowmeter 5200, a multiple-phase flow error correction module with one or more neural networks receives, as input, the physical parameters from the sensor and driver parameters processing module 6210, the raw mass-flow measurement signal, and a density measurement 6214 that is calculated as described above. For example, if the process fluid involves a known two-phase (e.g., gas and liquid constituents), three-phase (e.g., gas and two-liquid constituents) or other multiple-phase flow (e.g., one or more gas and one or more liquid constituents), the determination of a flow condition state may not be necessary, in this example, the process fluid may be a wet-gas that is already known to include a gas volume fraction (gvf) and liquid volume fraction (lvf). The wet gas may include, for example, natural gas, a liquid petroleum product, and water. Accordingly, the mass-flow measurement below can automatically determine a mass-flow measurement of each phase of a multi-phase process fluid. A dedicated neural network for each multiple phase flow condition can be used in the multiple phase flow error correction module 6220. Alternatively, or additionally, a single neural network that recognizes two-phase and/or three-phase (or more constituent phases) flow conditions and applies correction factors based on the actual multiple-phase flow condition may be used.

During a multi-phase flow condition, a multiple-phase flow error correction module 6220 receives the raw (or apparent) mass-flow measurement signal and the raw density signal. The apparent mass-flow measurement and density signals reflect the mass flow and density of the multi-phase process fluid rather than the mass flow and density of each of the phases included in the multi-phase process fluid. The multiple-phase flow error correction module 6220 includes a neural network processor for predicting a mass-flow error that results from the presence of the multi-phase process fluid. The neural network processor can be implemented in a software routine, or alternatively may be implemented as a separate programmed hardware processor. Operation of the neural network processor is described in greater detail below.

The inputs to the neural network processor may be apparent intermediate values determined from the raw mass-flow measurement signal and the density measurement. In this implementation, the multiple-phase flow error correction module 6220 determines apparent intermediate values, such as the apparent intermediate values discussed above with respect to FIG. 59, from the raw (or apparent) mass flow rate and density of the multi-phase process fluid. The apparent intermediate values are input into the neural network processor and corrected. The corrected apparent intermediate values are output to a mass-flow measurement output block 6230. In other implementations, the apparent (or raw) mass-flow measurement and density may be input to the neural network.

A neural network coefficients and training module 6225 stores a predetermined set or sets of neural network coefficients that are used by the neural network processor for each multiple-phase flow condition. The neural network coefficients and training module 6225 also may perform an online training function using training data so that an updated set of coefficients can be calculated for use by the neural network. While the predetermined set of neural network coefficients are generated through extensive laboratory testing and experiments based upon known two-phase, three-phase, or higher-phase mass-flow rates, the online training function performed by module 6225 may occur at the initial commissioning stage of the flowmeter, or may occur each time the flowmeter is initialized.

The corrected intermediate values from the neural network are input to the mass-flow measurement output block 6230. Using the corrected intermediate values, the mass-flow measurement output block 6230 determines the mass flow rate of each phase of the multi-phase process fluid, in some implementations, the measurement output block 6230 validates the mass-flow measurements for the phases and may perform an uncertainty analysis to generate an uncertainty parameter associated with the validation. The sensor parameters processing module 6210 also inputs a damping parameter and an amplitude of oscillation parameter (previously described) to an amplitude control module 6235. The amplitude control module 6235 further processes the damping parameter and the amplitude of oscillation parameter and generates digital drive signals. The digital drive signals are converted to analog drive signals by D/A converters 6240 for operating the drivers 6245 connected to the flowtube of the digital flowmeter, in some implementations, the amplitude control module 6235 may process the damping parameter and the amplitude of oscillation parameter and generate analog drive signals for operating the drivers 6245 directly.

FIG. 61 shows a procedure 6250 performed by the digital controller 6200. After processing begins (6251), measurement signals generated by the process sensors 6204 and drivers 6245 are quantified through an analog to digital conversion process (as described above), and the memory buffers 6208 are filled with the digital sensor and driver data (6252). For every new processing cycle, the sensor and driver parameters processing module 6210 retrieves the sensor and driver data from the buffers 6208 and calculates sensor and driver variables from the sensor data (6254). In particular, the sensor and driver parameters processing module 6210 calculates sensor voltages, sensor frequencies, drive current, and drive gain.

The sensor and driver parameters processing module 6210 executes an optional diagnose_flow_condition processing routine (6256) to calculate statistical values including the mean, standard deviation, and slope for each of the sensor and driver variables. The optional diagnose_flow_condition processing routine (-6256) may be utilized, for example, to identify a two-phase flow condition and/or to determine if a liquid component of the two-phase flow condition includes separate liquid constituents, such as oil and water. Based upon the statistics calculated for each of the sensor and driver variables, an optional flow condition state machine (6258) may be utilized to detect transitions between one of three valid flow-condition states: FLOW_CONDITION_SHOCK, FLOW_CONDITION_HOMOGENEOUS, AND FLOW_CONDITION_MIXED. However, if a process fluid is known to already include a heteregenous mixture, the process may automatically proceed from step 6254 to a calculation of raw mass-flow measurement 6260.

If the state FLOW_CONDITION_SHOCK is detected (6258), the mass-flow measurement analysis process is not performed due to irregular sensor inputs. On exit from this condition, the processing routine starts a new cycle (6251). The processing routine then searches for a new sinusoidal signal to track within the sensor signal data and resumes processing. As part of this tracking process, the processing routine must find the beginning and end of the sine wave using the zero crossing technique described above. If the state FLOW_CONDITION_SHOCK is not detected, the processing routine calculates the raw mass-flow measurement of the material flowing through the flowmeter 100 (6260).

If a multiple-phase flow is already known to exist in the monitored process, the material flowing through the flowmeter 100 is assumed to be, for example, a two-phase or three-phase material. For example, the material flowing through the flowmeter 100 may be a multi-phase process fluid, such as a wet gas. In this case, the multiple-phase flow error correction module 6220 determines an apparent intermediate value and, using the neural network processor(s), corrects the apparent intermediate value using (6274). Phase-specific properties of each phase of the multi-phase fluid are determined by the mass-flow measurement output block 6230 using the corrected intermediate value (6276). Processing then begins a new cycle (6251).

Referring again to FIG. 60, the neural network processor forming part of the two-phase flow error correction module 6220 may be a feed-forward neural network that provides a non-parametric framework for representing a non-linear functional mapping between an input and an output space. Of the various neural network models available, the multi-layer perceptron (MLP) and the radial basis function (RBF) networks have been used for implementations of the digital flowmeter. The MLP with one hidden layer (each unit having a sigmoidal activation function) can approximate arbitrarily well any continuous mapping.

In one example, a digital flowmeter 6200 may process a flow known to be a three-phase fluid. For example, the three-phase flow may be primarily natural gas, with a liquid constituent which includes a mixture of oil and water. In other examples, the same or a similar process may be applied to a two-phase fluid or a fluid containing more than three constituents in the fluid mixture.

Specifically, flowtube operation is maintained in a three-phase flow. The basic measurements of frequency and phase, sensor amplitudes and drive gain are obtained from the sensor signals and required current. The basic measurements are used, with any available external inputs and process or application-specific knowledge to generate estimates of the overall fluid and multi-component mass and volumetric flow rates.

For example, the estimates of the overall fluid and multi-component mass and volumetric flow rates may be generated as follows. The estimates of frequency, phase, and/or amplitudes may be improved using known correlations between the values, such as, for example, a rate of change of amplitude correction. The raw estimates of the mixed mass flow and density may be produced from best estimates of frequency, phase, flowtube an temperature and calibration constants. A simple linear correction is applied to the density measurement for the observed fluid pressure. In some implementations, the observed fluid pressure may be obtained from an external input. Because pressure expands and stiffens the flowtube, which may cause the raw density to be in error, a simple variable offset may work well if gas densities expected in a repeatable process or fluid mixture, while a more complete correction may include extra terms for the variable fluid density if changes in the liquid and/or gas constituent concentrations are anticipated in the process. A transmitter may include configuration parameters defining the expected liquid densities (with temperature compensation) and the gas reference density.

In a three-phase fluid mixture, a fixed water-cut (WC) may be assumed or may be measured. The water-cut is the portion of the mixture that is water. The fluid temperature is measured to calculate an estimate of the true fluid density (D₁) from the water-cut and the pure oil density (D_(oil)) and water density (D_(water)). The estimate of the true fluid density accounts for the known variation of D_(oil) and D_(water) with the fluid temperature and the fluid pressure.

D ₁=WC %/100*Dwater+(1−WC %/100)*Doil

A model (based on, for example, the ideal gas model) for the variation of gas density (D_(g)) with observed fluid pressure and fluid temperature, which may be obtained from external inputs, is assumed and the raw liquid volume fraction (raw_LVF) from the raw mixture density (raw_Dm) is calculated using

raw_LVF=100*(raw_(—) Dm−Dg)/(D1−Dg).

The raw volumetric mixture flowrates from the raw mixture are calculated using

raw_(—) mvf=raw_(—) mmf/raw_(—) Dm.

A neural network trained with experimental data is used to generate corrected estimates of the raw liquid volume fraction and the raw volumetric flowrates as shown below. In the equations below, the variable “nnfunction” represents the neural network.

corrected_LVF=nnfunction(raw_LVF,raw_mvf,fluid_pressure,flowtube_(—) DP)

corrected_(—) mvf=nnfunction(raw_LVF,raw_(—) mvf,fluid_pressure,flowtube_(—) DP)

The raw liquid volume fraction (raw LVF) equals 100−gas volume fraction (GVF). Additionally, the raw liquid volume fraction is closely related to density drop. The raw volumetric flow can be scaled as velocity for example without change in approach, the Neural nets could be combined, but could use different inputs.

The liquid and gas flowrates are calculated using the following relationships:

corr_liqvf=corr_LVF/100*corr_(—) mvf

corr_gasvf=(1−corr_LVF/100)*corr_(—) mvf

corr_liqmf=corr_liqvf*D1

corr_gasmf=corr_gasvf*Dg

A water cut meter can be used to provide measurement used as an additional input to the neural network(s), and to help accurately separate the liquid flow into the constituent parts. To help accurately separate the liquid flow, the following relationships may be used:

corr_Watervf=WC %/100*corr_liqvf

corr_Oilvf=(1−WC %/100)*corr_liqvf

corr_watermf=corr_Watervf*Dwater

corr_Oilmf=corr_Oilvf*Doil

Alternatively or additionally, apparent gas and non-gas Froude numbers may be determined, corrected using the neural network, and then used in determining the mass flowrates of the constituent components of the multiphase fluid. For example, the gas Froude number may be determined based on the following equation where is the apparent gas mass flow rate, ρ_(g) is an estimate of the density of the gas phase of the multi-phase fluid based on the ideal gas laws, ρ_(l) is an estimate of the density of the liquid in the non-gas phase of the multi-phase fluid, A is the cross-sectional area of the flowtube, D is the diameter of the flowtube, and g is the acceleration due to gravity. The apparent gas mass flow rate is a function of the known or assumed density of the component fluids in the multi-phase flow, the apparent density of the multi-phase fluid (the apparent bulk density), and the apparent mass flow rate of the multi-phase fluid (apparent bulk mass flow rate).

${Fr}_{g}^{a} = {{\frac{m_{g}^{a}}{\rho_{g}A\sqrt{g \cdot D}}\sqrt{\frac{\rho_{g}}{\rho_{l} - \rho_{g}}}} = {K \cdot V_{g}^{a} \cdot \sqrt{\frac{\rho_{g}}{\rho_{l} - \rho_{g}}}}}$

where

${K = \frac{1}{\sqrt{g \cdot D}}},$

the Apparent Gas Velocity

$V_{g}^{a} = {\frac{m_{g}^{a}}{\rho_{g}A}.}$

Similarly, the apparent non-gas Froude number (which may be the liquid Froude number) may be calculated using the following equation, where m_(l) ^(a) is the apparent liquid mass flow rate, K is the constant defined above with respect to the gas Froude number, and V_(t) ^(a) is the apparent liquid velocity determined similarly to the apparent as velocity shown above:

${Fr}_{l}^{a} = {{\frac{m_{l}^{a}}{\rho_{l}A\sqrt{g \cdot D}}\sqrt{\frac{\rho_{l}}{\rho_{l} - \rho_{g}}}} = {K \cdot V_{l}^{a} \cdot {\sqrt{\frac{\rho_{l}}{\rho_{l} - \rho_{g}}}.}}}$

The apparent gas and non-gas Froude numbers are then corrected using the neural network:

corrected_gas Froude number=nnfunction(apparent gas Froude number,apparent non-gas Froude number)

corrected_non-gas Froude number=nnfunction(apparent gas Froude number,apparent non-gas Froude number)

Once the corrected gas and non-gas Froude numbers are determined, the mass flow rate for the gas and non-gas components of the multi-phase fluid may be determined. In particular, once the corrected values of the gas and non-gas Froude numbers are obtained, all parameter values for the non-gas and gas components of the multi-phase fluid other than the mass flow rate are known. Thus, the corrected mass flow rate of the non-gas and gas components of the multi-phase fluid may be determined based on the equations above that are used to determine the apparent Froude numbers.

Additionally, as with the implementation using the liquid volume fraction and the volumetric flow as inputs to the neural network, a watercut meter may be used to help separate the multi-phase, fluid into constituent parts. For example, the watercut meter may provide a watercut (WC) of the multi-phase fluid that indicates the portion of the multi-phase fluid that is water, and the WC may be used to help separate the multi-phase fluid into constituent parts using the following equations:

corr_Watervf=WC %/100*corr_liqvf

corr_Oilvf=(1−WC %/100)*corr_liqvf

corr_Watermf=corr_Watervf*Dwater

corr_Oilmf=corr_Oilvf*Doil.

As discussed with respect to FIG. 59, in certain instances, the neural network may produce more accurate corrections of the apparent gas and non-gas Froude numbers than other apparent intermediate values. Thus, using the apparent gas and non-gas Froude numbers as the inputs to the neural network may result in a more accurate determination of the properties of the component flows that make up the multi-phase fluid.

The above description provides an overview of various digital Coriolis mass flowmeters, describing the background, implementation, examples of its operation, and a comparison to prior analog controllers and transmitters. A number of improvements over analog controller performance have been achieved, including: high precision control of flowtube operation, even with operation at very low amplitudes; the maintenance of flowtube operation even in highly damped conditions; high precision and high speed measurement; compensation for dynamic changes in amplitude; compensation for two-phase flow, and batching to/from empty. This combination of benefits suggests that the digital mass flowmeter represents a significant step forward, not simply a gradual evolution from analog technology. The ability to deal with two-phase flow and external vibration means that the digital mass flowmeter 100 gives improved performance in conventional Coriolis applications while expanding the range of applications to which the flow technology can be applied. The digital platform also is a useful and flexible vehicle for carrying out research into Coriolis metering in that it offers high precision, computing power, and data rates.

An additional application of the digital flowmeter 6200 to a three-phase fluid, e.g., a wet-gas having a gas (methane) and liquid component (oil and water) is described and shown in connection with FIGS. 62-72, FIG. 62 is a graphical view of a test matrix for wellheads tested based on actual testing at various well pressures and gas velocities. FIG. 62 is a graphical view of raw density errors at various liquid void fraction percentages and of wells at various velocities and pressures. FIG. 64 is a graphical view of raw mass flow errors at various liquid void fraction percentages and of wells at various velocities and pressures. FIG. 65 is a graphical view of raw liquid void fraction errors of wells at various velocities and pressures. FIG. 66 is a graphical view of raw volumetric flow errors for wells at various velocities and pressures. FIG. 67 is a graphical view of corrected liquid void fractions of wells at various velocities and pressures. FIG. 68 is a graphical view of corrected mixture volumetric flow of wells at various velocities and pressures. FIG. 69 is a graphical view of corrected gas mass flow of wells at various velocities and pressures. FIG. 70 is a graphical view of corrected gas cumulative probability of the digital flowmeter tested. FIG. 71 is a graphical view of corrected liquid mass flow error of wells at various velocities and pressures. FIG. 72 is a graphical view of corrected gas cumulative probability of the digital flowmeter tested.

With respect to FIGS. 62-72, test trials on wet gas, containing water and air, covered test well parameters throughout a wide rang of the meter. The Liquid Volumetric Flowrate Fraction (LVF===100%-GVF) points covered, included: 0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0%. With respect to the mass flow and density errors detected, errors for gas/liquid mixture mass flow and density assumed no liquid hold-up, a static mixer to control a stable flow profile in the field, and positive density errors due to liquid hold-up in meter. The positive density errors due to liquid hold-up in the meter are at the highest at low flows and at low gas densities. The negative mass flow errors were similar to Coriolis meter two-phase responses.

Applicable modeling strategies use apparent mass flow and apparent density to apply correction factors or curve-fitting of collected data to create real measurements, such as a real-time density measurement. However, the wide range of gas densities, such as 175-900 psi, favors additional approaches as well. For example, alternative parameters have been identified, including model parameters that include two principle parameters for errors. Specifically, mixture volumetric flow (assuming no slip between phases)—based on mass/density ratio and Liquid Volume Fraction (LVF) i.e. 100%−GVF. Corrections for each are provided, using their raw values and additional pressure data (only). Given corrected values of LVF and volumetric flow, the mass flow rates of the gas and liquid components can be calculated as follows:

ml=ρl·LVF/100%·Volflow

mg=ρg·(1−LVF/100%)·Volflow

The resulting errors are shown in FIGS. 69-72. The model covers a wide range of conditions, including various pressures, and flowrates. A more restricted set of conditions may yield improved results, such as, for example, a higher pressure resulting in smaller raw errors, a “natural” operating range for the meter, very high pressure drops with high LVF and velocity, and/or a need to review meter sizing with wet gas.

The model may be expanded or modified so that basic pressure “corrections,” which may include fitting raw data through curve-fitting to directly output actual measurements, e.g., without a true correction factor, and are applied to density before we apply the neural net. Current inputs are fluid specific, e.g., volumetric flow depends on actual fluid density. The inputs may be made less dimensional, for example, by converting volumetric flow to velocity, then express the velocity as a percentage of maximum velocity that may be accommodated by the conduit, followed by normalizing the data to determine constituents. Operating pressures may include a 60 bar flow pressure, with a 2-3 bar differential, and will support higher operating pressures in the range of 50 psi-1000 psi. The detailed model calibration trials referenced in FIGS. 62-72 utilize natural gas in the range of approximately 375 psi from a wellhead. The flowtube sizing may also be deter pined based on the pressure drop.

R. Source Code Listing

The following source code, which is hereby incorporated into this application, is used to implement the mass-flow rate processing routine in accordance with one implementation of the flowmeter. It will be appreciated that it is possible to implement the mass-flow rate processing routine using different computer code without departing from the scope of the described techniques. Thus, neither the foregoing description nor the following source code listing is intended to limit the described techniques.

Source Code Listing

void calculate_massflow(meas_data_type *p, meas_data_type *op, int validating) { double Tz, z1, z2, z3, z4, z5, z6, z7, t, x, dd, m, g, flow_error; double noneu_mass_flow, phase_bias_unc, phase_prec_unc, this_density; int reset, freeze; /* calculate non- engineering units mass flow */ if (amp_sv1 < 1e−6) noneu_mass_flow = 0.0; else noneu_mass_flow = tan(my_pi * p−>phase_diff/180); /* convert to engineering units */ Tz = p−>temperature_value − 20; p−>massflow_value = flow_factor * 16.0 * (FC1 * Tz + FC3 * Tz*Tz + FC2) * noneu_mass_flow /p−>v_freq; /* apply two-phase flow correction if necessary */ if (validating && do_two_phase_correction) { /* call neural net for calculation of mass flow correction */ t = VMV_temp_stats.mean; // mean VMV temperature x = RMV_dens_stats.mean; // mean RMV density dd = (TX_true_density − x) / TX_ true_density * 100.0; m = RMV_mass_stats.mean; // mean RMV mass flow; g = gain_stats.mean; // mean gain; nn_predict(t, dd, m, g, &flow_error); p−>massflow_value = 100.0*m/(100.0 + flow_error); }

S. Notice of Copyright

A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by any one of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.

Other implementations are within the scope of the following claims. 

1. A method comprising: passing a multi-phase process fluid through a vibratable flowtube; inducing motion in the vibratable flowtube; collecting sensor data from a sensor attached to the flowtube; determining frequency from collected sensor data; comparing with an expected frequency range; eliminating a zero offset when frequency is within the expected range; determining an amplitude; determining a phase; and calculating a drive signal using the amplitude and phase.
 2. The method of claim 1, wherein the zero offset is provided from a zero offset compensation procedure.
 3. The method of claim 1, wherein the amplitude is determined using a Fourier-based technique.
 4. The method of claim 1, wherein the phase is determined using a Fourier-based technique.
 5. The method of claim 1, wherein the calculated drive signal is feed back into the positive drive signal to vibrate the flow tube.
 6. The method of claim 1, wherein the frequency is outside the expected range and setting a stall signal for subsequent communication. 